// ---------------------------------------------------------------------
//
// Copyright (C) 2001 - 2022 by the deal.II authors
//
// This file is part of the deal.II library.
//
// The deal.II library is free software; you can use it, redistribute
// it, and/or modify it under the terms of the GNU Lesser General
// Public License as published by the Free Software Foundation; either
// version 2.1 of the License, or (at your option) any later version.
// The full text of the license can be found in the file LICENSE.md at
// the top level directory of deal.II.
//
// ---------------------------------------------------------------------

#ifndef dealii_grid_tools_h
#define dealii_grid_tools_h


#include <deal.II/base/config.h>

#include <deal.II/base/bounding_box.h>
#include <deal.II/base/geometry_info.h>
#include <deal.II/base/point.h>
#include <deal.II/base/std_cxx17/optional.h>

#include <deal.II/boost_adaptors/bounding_box.h>

#include <deal.II/distributed/shared_tria.h>

#include <deal.II/dofs/dof_handler.h>

#include <deal.II/fe/fe_values.h>
#include <deal.II/fe/mapping.h>
#include <deal.II/fe/mapping_q1.h>

#include <deal.II/grid/manifold.h>
#include <deal.II/grid/tria.h>
#include <deal.II/grid/tria_accessor.h>
#include <deal.II/grid/tria_iterator.h>

#include <deal.II/hp/dof_handler.h>

#include <deal.II/lac/la_parallel_vector.h>
#include <deal.II/lac/la_vector.h>
#include <deal.II/lac/petsc_vector.h>
#include <deal.II/lac/sparsity_tools.h>
#include <deal.II/lac/trilinos_vector.h>

#include <deal.II/numerics/rtree.h>

DEAL_II_DISABLE_EXTRA_DIAGNOSTICS
#include <boost/archive/binary_iarchive.hpp>
#include <boost/archive/binary_oarchive.hpp>
#include <boost/random/mersenne_twister.hpp>
#include <boost/serialization/array.hpp>
#include <boost/serialization/vector.hpp>

#ifdef DEAL_II_WITH_ZLIB
#  include <boost/iostreams/device/back_inserter.hpp>
#  include <boost/iostreams/filter/gzip.hpp>
#  include <boost/iostreams/filtering_stream.hpp>
#  include <boost/iostreams/stream.hpp>
#endif
DEAL_II_ENABLE_EXTRA_DIAGNOSTICS

#include <bitset>
#include <list>
#include <set>

DEAL_II_NAMESPACE_OPEN

// Forward declarations
#ifndef DOXYGEN
namespace parallel
{
  namespace distributed
  {
    template <int, int>
    class Triangulation;
  }
} // namespace parallel

namespace hp
{
  template <int, int>
  class MappingCollection;
}

class SparsityPattern;

namespace GridTools
{
  template <int dim, int spacedim>
  class Cache;
}
#endif

namespace internal
{
  template <int dim, int spacedim, class MeshType>
  class ActiveCellIterator
  {
  public:
#ifndef _MSC_VER
    using type = typename MeshType::active_cell_iterator;
#else
    using type = TriaActiveIterator<dealii::CellAccessor<dim, spacedim>>;
#endif
  };

#ifdef _MSC_VER
  template <int dim, int spacedim>
  class ActiveCellIterator<dim, spacedim, dealii::DoFHandler<dim, spacedim>>
  {
  public:
    using type =
      TriaActiveIterator<dealii::DoFCellAccessor<dim, spacedim, false>>;
  };
#endif
} // namespace internal

/**
 * This namespace is a collection of algorithms working on triangulations,
 * such as shifting or rotating triangulations, but also finding a cell that
 * contains a given point. See the descriptions of the individual functions
 * for more information.
 *
 * @ingroup grid
 */
namespace GridTools
{
  /**
   * @name Information about meshes and cells
   */
  /*@{*/

  /**
   * Return the diameter of a triangulation. The diameter is computed using
   * only the vertices, i.e. if the diameter should be larger than the maximal
   * distance between boundary vertices due to a higher order mapping, then
   * this function will not catch this.
   */
  template <int dim, int spacedim>
  double
  diameter(const Triangulation<dim, spacedim> &tria);

  /**
   * Compute the volume (i.e. the dim-dimensional measure) of the
   * triangulation. We compute the measure using the integral $\sum_K \int_K 1
   * \; dx$ where $K$ are the cells of the given triangulation. The integral
   * is approximated via quadrature for which we need the mapping argument.
   *
   * If the triangulation is a dim-dimensional one embedded in a higher
   * dimensional space of dimension spacedim, then the value returned is the
   * dim-dimensional measure. For example, for a two-dimensional triangulation
   * in three-dimensional space, the value returned is the area of the surface
   * so described. (This obviously makes sense since the spacedim-dimensional
   * measure of a dim-dimensional triangulation would always be zero if dim @<
   * spacedim.
   *
   * This function also works for objects of type
   * parallel::distributed::Triangulation, in which case the function is a
   * collective operation.
   *
   * @param tria The triangulation.
   * @param mapping An optional argument used to denote the mapping that
   * should be used when describing whether cells are bounded by straight or
   * curved faces. The default is to use a $Q_1$ mapping, which corresponds to
   * straight lines bounding the cells.
   * @return The dim-dimensional measure of the domain described by the
   * triangulation, as discussed above.
   */
  template <int dim, int spacedim>
  double
  volume(const Triangulation<dim, spacedim> &tria,
         const Mapping<dim, spacedim> &      mapping =
           (ReferenceCells::get_hypercube<dim>()
#ifndef _MSC_VER
              .template get_default_linear_mapping<dim, spacedim>()
#else
              .ReferenceCell::get_default_linear_mapping<dim, spacedim>()
#endif
              ));

  /**
   * Return an approximation of the diameter of the smallest active cell of a
   * triangulation. See step-24 for an example of use of this function.
   *
   * Notice that, even if you pass a non-trivial mapping, the returned value is
   * computed only using information on the vertices of the triangulation,
   * possibly transformed by the mapping. While this is accurate most of the
   * times, it may fail to give the correct result when the triangulation
   * contains very distorted cells.
   */
  template <int dim, int spacedim>
  double
  minimal_cell_diameter(
    const Triangulation<dim, spacedim> &triangulation,
    const Mapping<dim, spacedim> &      mapping =
      (ReferenceCells::get_hypercube<dim>()
#ifndef _MSC_VER
         .template get_default_linear_mapping<dim, spacedim>()
#else
         .ReferenceCell::get_default_linear_mapping<dim, spacedim>()
#endif
         ));

  /**
   * Return an approximation of the diameter of the largest active cell of a
   * triangulation.
   *
   * Notice that, even if you pass a non-trivial mapping to this function, the
   * returned value is computed only using information on the vertices of the
   * triangulation, possibly transformed by the mapping. While this is accurate
   * most of the times, it may fail to give the correct result when the
   * triangulation contains very distorted cells.
   */
  template <int dim, int spacedim>
  double
  maximal_cell_diameter(
    const Triangulation<dim, spacedim> &triangulation,
    const Mapping<dim, spacedim> &      mapping =
      (ReferenceCells::get_hypercube<dim>()
#ifndef _MSC_VER
         .template get_default_linear_mapping<dim, spacedim>()
#else
         .ReferenceCell::get_default_linear_mapping<dim, spacedim>()
#endif
         ));

  /**
   * Given a list of vertices (typically obtained using
   * Triangulation::get_vertices) as the first, and a list of vertex indices
   * that characterize a single cell as the second argument, return the
   * measure (area, volume) of this cell. If this is a real cell, then you can
   * get the same result using <code>cell-@>measure()</code>, but this
   * function also works for cells that do not exist except that you make it
   * up by naming its vertices from the list.
   *
   * @deprecated Use the more general function which takes an ArrayView instead.
   */
  template <int dim>
  DEAL_II_DEPRECATED double
  cell_measure(
    const std::vector<Point<dim>> &all_vertices,
    const unsigned int (&vertex_indices)[GeometryInfo<dim>::vertices_per_cell]);

  /**
   * Given a list of vertices (typically obtained using
   * Triangulation::get_vertices()) as the first, and a list of vertex indices
   * that characterize a single cell as the second argument, return the
   * measure (area, volume) of this cell. If this is a real cell, then you can
   * get the same result using <code>cell-@>measure()</code>, but this
   * function also works for cells that do not exist except that you make it
   * up by naming its vertices from the list.
   *
   * The size of @p vertex_indices, combined with `dim`, implicitly encodes
   * the ReferenceCell type of the provided cell. For example, if `dim == 2` and
   * `vertex_indices.size() == 3` then the cell is a triangle, but if
   * `dim == 2` and `vertex_indices.size() == 4` then the cell is a
   * quadrilateral. A std::vector is implicitly convertible to an ArrayView, so
   * it can be passed directly to this function. See the ArrayView class for
   * more information.
   *
   * @note This function is only implemented for codimension zero objects.
   */
  template <int dim>
  double
  cell_measure(const std::vector<Point<dim>> &      all_vertices,
               const ArrayView<const unsigned int> &vertex_indices);

  /**
   * This function computes an affine approximation of the map from the unit
   * coordinates to the real coordinates of the form $p_\text{real} = A
   * p_\text{unit} + b $ by a least squares fit of this affine function to the
   * $2^\text{dim}$ vertices representing a quadrilateral or hexahedral cell
   * in `spacedim` dimensions. The result is returned as a pair with the
   * matrix <i>A</i> as the first argument and the vector <i>b</i> describing
   * distance of the plane to the origin.
   *
   * For any valid mesh cell whose geometry is not degenerate, this operation
   * results in a unique affine mapping, even in cases where the actual
   * transformation by a bi-/trilinear or higher order mapping might be
   * singular. The result is exact in case the transformation from the unit to
   * the real cell is indeed affine, such as in one dimension or for Cartesian
   * and affine (parallelogram) meshes in 2D/3D.
   *
   * This approximation is underlying the function
   * TriaAccessor::real_to_unit_cell_affine_approximation() function.
   *
   * For exact transformations to the unit cell, use
   * Mapping::transform_real_to_unit_cell().
   */
  template <int dim, int spacedim>
  std::pair<DerivativeForm<1, dim, spacedim>, Tensor<1, spacedim>>
  affine_cell_approximation(const ArrayView<const Point<spacedim>> &vertices);

  /**
   * Computes an aspect ratio measure for all locally-owned active cells and
   * fills a vector with one entry per cell, given a @p triangulation and
   * @p mapping. The size of the vector that is returned equals the number of
   * active cells. The vector contains zero for non locally-owned cells. The
   * aspect ratio of a cell is defined as the ratio of the maximum to minimum
   * singular value of the Jacobian, taking the maximum over all quadrature
   * points of a quadrature rule specified via @p quadrature. For example, for
   * the special case of rectangular elements in 2d with dimensions $a$ and $b$
   * ($a \geq b$), this function returns the usual aspect ratio definition
   * $a/b$. The above definition using singular values is a generalization to
   * arbitrarily deformed elements. This function is intended to be used for
   * $d=2,3$ space dimensions, but it can also be used for $d=1$ returning a
   * value of 1.
   *
   * @note Inverted elements do not throw an exception. Instead, a value of inf
   * is written into the vector in case of inverted elements.
   *
   * @note Make sure to use enough quadrature points for a precise calculation
   * of the aspect ratio in case of deformed elements.
   *
   * @note In parallel computations the return value will have the length
   * n_active_cells but the aspect ratio is only computed for the cells that
   * are locally owned and placed at index CellAccessor::active_cell_index(),
   * respectively. All other values are set to 0.
   */
  template <int dim>
  Vector<double>
  compute_aspect_ratio_of_cells(const Mapping<dim> &      mapping,
                                const Triangulation<dim> &triangulation,
                                const Quadrature<dim> &   quadrature);

  /**
   * Computes the maximum aspect ratio by taking the maximum over all cells.
   *
   * @note When running in parallel with a Triangulation that supports MPI,
   * this is a collective call and the return value is the maximum over all
   * processors.
   */
  template <int dim>
  double
  compute_maximum_aspect_ratio(const Mapping<dim> &      mapping,
                               const Triangulation<dim> &triangulation,
                               const Quadrature<dim> &   quadrature);

  /**
   * Compute the smallest box containing the entire triangulation.
   *
   * If the input triangulation is a `parallel::distributed::Triangulation`,
   * then each processor will compute a bounding box enclosing all locally
   * owned, ghost, and artificial cells. In the case of a domain without curved
   * boundaries, these bounding boxes will all agree between processors because
   * the union of the areas occupied by artificial and ghost cells equals the
   * union of the areas occupied by the cells that other processors own.
   * However, if the domain has curved boundaries, this is no longer the case.
   * The bounding box returned may be appropriate for the current processor,
   * but different from the bounding boxes computed on other processors.
   */
  template <int dim, int spacedim>
  BoundingBox<spacedim>
  compute_bounding_box(const Triangulation<dim, spacedim> &triangulation);

  /**
   * Return the point on the geometrical object @p object closest to the given
   * point @p trial_point. For example, if @p object is a one-dimensional line
   * or edge, then the returned point will be a point on the geodesic that
   * connects the vertices as the manifold associated with the object sees it
   * (i.e., the geometric line may be curved if it lives in a higher
   * dimensional space). If the iterator points to a quadrilateral in a higher
   * dimensional space, then the returned point lies within the convex hull of
   * the vertices of the quad as seen by the associated manifold.
   *
   * @note This projection is usually not well-posed since there may be
   * multiple points on the object that minimize the distance. The algorithm
   * used in this function is robust (and the output is guaranteed to be on
   * the given @p object) but may only provide a few correct digits if the
   * object has high curvature. If your manifold supports it then the
   * specialized function Manifold::project_to_manifold() may perform better.
   */
  template <typename Iterator>
  Point<Iterator::AccessorType::space_dimension>
  project_to_object(
    const Iterator &                                      object,
    const Point<Iterator::AccessorType::space_dimension> &trial_point);

  /**
   * Return the arrays that define the coarse mesh of a Triangulation. This
   * function is the inverse of Triangulation::create_triangulation().
   *
   * The return value is a tuple with the vector of vertices, the vector of
   * cells, and a SubCellData structure. The latter contains additional
   * information about faces and lines.
   *
   * This function is useful in cases where one needs to deconstruct a
   * Triangulation or manipulate the numbering of the vertices in some way: an
   * example is GridGenerator::merge_triangulations().
   */
  template <int dim, int spacedim>
  std::
    tuple<std::vector<Point<spacedim>>, std::vector<CellData<dim>>, SubCellData>
    get_coarse_mesh_description(const Triangulation<dim, spacedim> &tria);

  /*@}*/
  /**
   * @name Functions supporting the creation of meshes
   */
  /*@{*/

  /**
   * Remove vertices that are not referenced by any of the cells. This
   * function is called by all <tt>GridIn::read_*</tt> functions to eliminate
   * vertices that are listed in the input files but are not used by the cells
   * in the input file. While these vertices should not be in the input from
   * the beginning, they sometimes are, most often when some cells have been
   * removed by hand without wanting to update the vertex lists, as they might
   * be lengthy.
   *
   * This function is called by all <tt>GridIn::read_*</tt> functions as the
   * triangulation class requires them to be called with used vertices only.
   * This is so, since the vertices are copied verbatim by that class, so we
   * have to eliminate unused vertices beforehand.
   *
   * Not implemented for the codimension one case.
   */
  template <int dim, int spacedim>
  void
  delete_unused_vertices(std::vector<Point<spacedim>> &vertices,
                         std::vector<CellData<dim>> &  cells,
                         SubCellData &                 subcelldata);

  /**
   * Remove vertices that are duplicated, due to the input of a structured
   * grid, for example. If these vertices are not removed, the faces bounded
   * by these vertices become part of the boundary, even if they are in the
   * interior of the mesh.
   *
   * This function is called by some <tt>GridIn::read_*</tt> functions. Only
   * the vertices with indices in @p considered_vertices are tested for
   * equality. This speeds up the algorithm, which is, for worst-case hyper
   * cube geometries $O(N^{3/2})$ in 2D and $O(N^{5/3})$ in 3D: quite slow.
   * However, if you wish to consider all vertices, simply pass an empty
   * vector. In that case, the function fills @p considered_vertices with all
   * vertices.
   *
   * Two vertices are considered equal if their difference in each coordinate
   * direction is less than @p tol.
   */
  template <int dim, int spacedim>
  void
  delete_duplicated_vertices(std::vector<Point<spacedim>> &all_vertices,
                             std::vector<CellData<dim>> &  cells,
                             SubCellData &                 subcelldata,
                             std::vector<unsigned int> &   considered_vertices,
                             const double                  tol = 1e-12);

  /**
   * Grids generated by grid generators may have an orientation of cells which
   * is the inverse of the orientation required by deal.II.
   *
   * In 2d and 3d this function checks whether all cells have negative or
   * positive measure/volume. In the former case, all cells are inverted. It
   * does nothing in 1d.
   *
   * The inversion of cells might also work when only a subset of all cells
   * have negative volume. However, grids consisting of a mixture of negative
   * and positively oriented cells are very likely to be broken. Therefore, an
   * exception is thrown, in case cells are not uniformly oriented.
   *
   * @note This function should be called before GridTools::consistently_order_cells().
   *
   * @param all_vertices The vertices of the mesh.
   * @param cells The array of CellData objects that describe the mesh's topology.
   */
  template <int dim, int spacedim>
  void
  invert_all_negative_measure_cells(
    const std::vector<Point<spacedim>> &all_vertices,
    std::vector<CellData<dim>> &        cells);

  /**
   * Check the given cells and inverts any cell that is considered to have
   * negative measure/volume in the orientation required by deal.II.
   *
   * This function is identical to invert_all_negative_measure_cells() except it
   * does not throw an error if only some of the cells are inverted.  Instead,
   * this function returns how many cells were inverted.  Additionally, it will
   * always throw an exception outside of codimension 0.
   */
  template <int dim, int spacedim>
  std::size_t
  invert_cells_with_negative_measure(
    const std::vector<Point<spacedim>> &all_vertices,
    std::vector<CellData<dim>> &        cells);

  /**
   * Given a vector of CellData objects describing a mesh, reorder their
   * vertices so that all lines are consistently oriented.
   *
   * The expectations on orientation and a discussion of this function are
   * available in the
   * @ref reordering "reordering module".
   *
   * @param cells The array of CellData objects that describe the mesh's topology.
   */
  template <int dim>
  void
  consistently_order_cells(std::vector<CellData<dim>> &cells);

  /*@}*/
  /**
   * @name Rotating, stretching and otherwise transforming meshes
   */
  /*@{*/

  /**
   * Transform the vertices of the given triangulation by applying the
   * function object provided as first argument to all its vertices.
   *
   * The transformation given as argument is used to transform each vertex.
   * Its respective type has to offer a function-like syntax, i.e. the
   * predicate is either an object of a type that has an <tt>operator()</tt>,
   * or it is a pointer to a non-member function, or it is a lambda function
   * object. In either case, argument and return
   * value have to be of type `Point@<spacedim@>`.
   *
   * @note The transformations that make sense to use with this function
   *   should have a Jacobian with a positive determinant. For example,
   *   rotation, shearing, stretching, or scaling all satisfy this (though
   *   there is no requirement that the transformation used actually is
   *   linear, as all of these examples are). On the other hand, reflections
   *   or inversions have a negative determinant of the Jacobian. The
   *   current function has no way of asserting a positive determinant
   *   of the Jacobian, but if you happen to use such a transformation,
   *   the result will be a triangulation in which cells have a negative
   *   volume.
   *
   * @note If you are using a parallel::distributed::Triangulation you will
   * have hanging nodes in your local Triangulation even if your "global" mesh
   * has no hanging nodes. This will cause issues with wrong positioning of
   * hanging nodes in ghost cells if you call the current functions: The
   * vertices of all locally owned cells will be correct, but the vertices of
   * some ghost cells may not. This means that computations like
   * KellyErrorEstimator may give wrong answers.
   *
   * @note This function is in general not compatible with manifolds attached
   * to the triangulation. For example, in order to refine the grid (using
   * manifolds) after the grid transformation, you have to make sure that
   * the original manifold is still valid for the transformed geometry. This
   * does not hold in general, and it is necessary to clear manifolds from
   * the triangulation (for example, using Triangulation::clear_all_manifolds())
   * before the transformation, and then attach new ones after the
   * transformation that are valid for the transformed geometry. There are cases
   * where this is awkward, most notably if you are using a mesh generated by
   * the functions in GridGenerator which generally attach suitable manifolds
   * upon mesh generation; in those cases, you will have to think about how
   * these manifolds were constructed, and create a manifold that is constructed
   * in a similar way but applies to the transformed geometry. As a consequence,
   * if you only care about manifolds for mesh refinement, it is often simpler
   * to just refine the original mesh before transformation as needed, and then
   * simply forget about the manifolds. Of course, manifolds are also used for
   * other cases (e.g., for normal vectors, curved edges and faces, and higher
   * order mappings), and if these are relevant to what you are doing, then
   * there is no alternative to building appropriate manifolds for the
   * transformed geometry and attaching these to the transformed geometry. In
   * general, detaching manifolds from a triangulation and then doing the
   * transformation would look as follows:
   * @code
   * ...
   * triangulation.reset_all_manifolds();
   * GridTools::transform(MyTransformation<dim>(), triangulation);
   * ...
   * @endcode
   *
   * This function is used in the "Possibilities for extensions" section of
   * step-38. It is also used in step-49 and step-53.
   */
  template <int dim, typename Transformation, int spacedim>
  void
  transform(const Transformation &        transformation,
            Triangulation<dim, spacedim> &triangulation);

  /**
   * Shift each vertex of the triangulation by the given shift vector. This
   * function uses the transform() function above, so the requirements on the
   * triangulation stated there hold for this function as well; in particular,
   * this is true about the discussion about manifolds.
   */
  template <int dim, int spacedim>
  void
  shift(const Tensor<1, spacedim> &   shift_vector,
        Triangulation<dim, spacedim> &triangulation);


  /**
   * Rotate all vertices of the given two-dimensional triangulation in
   * counter-clockwise sense around the origin of the coordinate system by the
   * given angle (given in radians, rather than degrees). This function uses
   * the transform() function above, so the requirements on the triangulation
   * stated there hold for this function as well; in particular,
   * this is true about the discussion about manifolds.
   *
   * @note This function is only supported for dim=2.
   */
  template <int dim>
  void
  rotate(const double angle, Triangulation<dim> &triangulation);

  /**
   * Rotate all vertices of the given @p triangulation in counter-clockwise
   * direction around the @p axis described by a unit vector. Otherwise like the
   * function above; in particular, this function calls the transform() function
   * and so the discussion about manifolds there also applies here.
   *
   * @param[in] angle Angle in radians to rotate the Triangulation by.
   * @param[in] axis A unit vector that defines the axis of rotation.
   * @param[in,out] triangulation The Triangulation object to rotate.
   *
   * @note Implemented for spacedim=3 and dim=1, 2, and 3.
   */
  template <int dim>
  void
  rotate(const Tensor<1, 3, double> &axis,
         const double                angle,
         Triangulation<dim, 3> &     triangulation);

  /**
   * Rotate all vertices of the given @p triangulation in counter-clockwise
   * direction around the axis with the given index. Otherwise like the
   * function above; in particular, this function calls the transform() function
   * and so the discussion about manifolds there also applies here.
   *
   * @param[in] angle Angle in radians to rotate the Triangulation by.
   * @param[in] axis Index of the coordinate axis to rotate around, keeping
   * that coordinate fixed (0=x axis, 1=y axis, 2=z axis).
   * @param[in,out] triangulation The Triangulation object to rotate.
   *
   * @note Implemented for dim=1, 2, and 3.
   *
   * @deprecated Use the alternative with the unit vector instead.
   */
  template <int dim>
  DEAL_II_DEPRECATED_EARLY void
  rotate(const double           angle,
         const unsigned int     axis,
         Triangulation<dim, 3> &triangulation);

  /**
   * Transform the given triangulation smoothly to a different domain where,
   * typically, each of the vertices at the boundary of the triangulation is
   * mapped to the corresponding points in the @p new_points map.
   *
   * The unknown displacement field $u_d(\mathbf x)$ in direction $d$ is
   * obtained from the minimization problem \f[ \min\, \int \frac{1}{2}
   *   c(\mathbf x)
   *   \mathbf \nabla u_d(\mathbf x) \cdot
   *   \mathbf \nabla u_d(\mathbf x)
   *   \,\rm d x
   * \f]
   * subject to prescribed constraints. The minimizer is obtained by solving the
   * Laplace equation of the dim components of a displacement field that maps
   * the current
   * domain into one described by @p new_points . Linear finite elements with
   * four Gaussian quadrature points in each direction are used. The difference
   * between the vertex positions specified in @p new_points and their current
   * value in @p tria therefore represents the prescribed values of this
   * displacement field at the boundary of the domain, or more precisely at all
   * of those locations for which @p new_points provides values (which may be
   * at part of the boundary, or even in the interior of the domain). The
   * function then evaluates this displacement field at each unconstrained
   * vertex and uses it to place the mapped vertex where the displacement
   * field locates it. Because the solution of the Laplace equation is smooth,
   * this guarantees a smooth mapping from the old domain to the new one.
   *
   * @param[in] new_points The locations where a subset of the existing
   * vertices are to be placed. Typically, this would be a map from the vertex
   * indices of all nodes on the boundary to their new locations, thus
   * completely specifying the geometry of the mapped domain. However, it may
   * also include interior points if necessary and it does not need to include
   * all boundary vertices (although you then lose control over the exact
   * shape of the mapped domain).
   *
   * @param[in,out] tria The Triangulation object. This object is changed in-
   * place, i.e., the previous locations of vertices are overwritten.
   *
   * @param[in] coefficient An optional coefficient for the Laplace problem.
   * Larger values make cells less prone to deformation (effectively
   * increasing their stiffness). The coefficient is evaluated in the
   * coordinate system of the old, undeformed configuration of the
   * triangulation as input, i.e., before the transformation is applied.
   * Should this function be provided, sensible results can only be expected
   * if all coefficients are positive.
   *
   * @param[in] solve_for_absolute_positions If set to <code>true</code>, the
   * minimization problem is formulated with respect to the final vertex
   * positions as opposed to their displacement. The two formulations are
   * equivalent for
   * the homogeneous problem (default value of @p coefficient), but they
   * result in very different mesh motion otherwise. Since in most cases one
   * will be using a non-constant coefficient in displacement formulation, the
   * default value of this parameter is <code>false</code>.
   *
   * @note This function is not currently implemented for the 1d case.
   */
  template <int dim>
  void
  laplace_transform(const std::map<unsigned int, Point<dim>> &new_points,
                    Triangulation<dim> &                      tria,
                    const Function<dim, double> *coefficient = nullptr,
                    const bool solve_for_absolute_positions  = false);

  /**
   * Return a std::map with all vertices of faces located in the boundary
   *
   * @param[in] tria The Triangulation object.
   */
  template <int dim, int spacedim>
  std::map<unsigned int, Point<spacedim>>
  get_all_vertices_at_boundary(const Triangulation<dim, spacedim> &tria);

  /**
   * Scale the entire triangulation by the given factor. To preserve the
   * orientation of the triangulation, the factor must be positive.
   *
   * This function uses the transform() function above, so the requirements on
   * the triangulation stated there hold for this function as well.
   */
  template <int dim, int spacedim>
  void
  scale(const double                  scaling_factor,
        Triangulation<dim, spacedim> &triangulation);

  /**
   * Distort the given triangulation by randomly moving around all the
   * vertices of the grid.  The direction of movement of each vertex is
   * random, while the length of the shift vector has a value of @p factor
   * times the minimal length of the active edges adjacent to this vertex.
   * Note that @p factor should obviously be well below <tt>0.5</tt>.
   *
   * If @p keep_boundary is set to @p true (which is the default), then
   * boundary vertices are not moved.
   *
   * @p seed is used for the initialization of the random engine. Its
   * default value initializes the engine with the same state as in
   * previous versions of deal.II.
   */
  template <int dim, int spacedim>
  void
  distort_random(
    const double                  factor,
    Triangulation<dim, spacedim> &triangulation,
    const bool                    keep_boundary = true,
    const unsigned int            seed = boost::random::mt19937::default_seed);

  /**
   * Remove hanging nodes from a grid. If the @p isotropic parameter is set
   * to @p false (default) this function detects cells with hanging nodes and
   * refines the neighbours in the direction that removes hanging nodes.
   * If the @p isotropic parameter is set
   * to @p true, the neighbours refinement is made in each directions.
   * In order to remove all hanging nodes this procedure has to be repeated:
   * this could require a large number of iterations.
   * To avoid this a max number (@p max_iterations) of iteration is provided.
   *
   * Consider the following grid:
   * @image html remove_hanging_nodes-hanging.png
   *
   * @p isotropic == @p false would return:
   * @image html remove_hanging_nodes-aniso.png
   *
   * @p isotropic == @p true would return:
   * @image html remove_hanging_nodes-isotro.png
   *
   * @param[in,out] tria Triangulation to refine.
   *
   * @param[in] isotropic If true refine cells in each directions, otherwise
   * (default value) refine the cell in the direction that removes hanging node.
   *
   * @param[in] max_iterations At each step only closest cells to hanging nodes
   * are refined. The code may require a lot of iterations to remove all
   * hanging nodes. @p max_iterations is the maximum number of iteration
   * allowed. If @p max_iterations == numbers::invalid_unsigned_int this
   * function continues refining until there are no hanging nodes.
   *
   * @note In the case of parallel codes, this function should be combined
   * with GridGenerator::flatten_triangulation.
   */
  template <int dim, int spacedim>
  void
  remove_hanging_nodes(Triangulation<dim, spacedim> &tria,
                       const bool                    isotropic      = false,
                       const unsigned int            max_iterations = 100);

  /**
   * Refine a mesh anisotropically such that the resulting mesh is composed by
   * cells with maximum ratio between dimensions less than @p max_ratio.
   * This procedure requires an algorithm that may not terminate. Consequently,
   * it is possible to set a maximum number of iterations through the
   * @p max_iterations parameter.
   *
   * Starting from a cell like this:
   * @image html remove_anisotropy-coarse.png
   *
   * This function would return:
   * @image html remove_anisotropy-refined.png
   *
   * @param[in,out] tria Triangulation to refine.
   *
   * @param[in] max_ratio Maximum value allowed among the ratio between
   * the dimensions of each cell.
   *
   * @param[in] max_iterations Maximum number of iterations allowed.
   *
   * @note In the case of parallel codes, this function should be combined
   * with GridGenerator::flatten_triangulation and
   * GridTools::remove_hanging_nodes.
   */
  template <int dim, int spacedim>
  void
  remove_anisotropy(Triangulation<dim, spacedim> &tria,
                    const double                  max_ratio      = 1.6180339887,
                    const unsigned int            max_iterations = 5);

  /**
   * Analyze the boundary cells of a mesh, and if one cell is found at
   * a corner position (with dim adjacent faces on the boundary), and its
   * dim-dimensional angle fraction exceeds @p limit_angle_fraction,
   * refine globally once, and replace the children of such cell
   * with children where the corner is no longer offending the given angle
   * fraction.
   *
   * If no boundary cells exist with two adjacent faces on the boundary, then
   * the triangulation is left untouched. If instead we do have cells with dim
   * adjacent faces on the boundary, then the fraction between the
   * dim-dimensional
   * solid angle and dim*pi/2 is checked against the parameter @p limit_angle_fraction.
   * If it is higher, the grid is refined once, and the children of the
   * offending cell are replaced with some cells that instead respect the limit.
   * After this process the triangulation is flattened, and all Manifold objects
   * are restored as they were in the original triangulation.
   *
   * An example is given by the following mesh, obtained by attaching a
   * SphericalManifold to a mesh generated using GridGenerator::hyper_cube:
   *
   * @code
   * const SphericalManifold<dim> m0;
   * Triangulation<dim> tria;
   * GridGenerator::hyper_cube(tria,-1,1);
   * tria.set_all_manifold_ids_on_boundary(0);
   * tria.set_manifold(0, m0);
   * tria.refine_global(4);
   * @endcode
   *
   * <p ALIGN="center">
   * @image html regularize_mesh_01.png
   * </p>
   *
   * The four cells that were originally the corners of a square will give you
   * some troubles during computations, as the jacobian of the transformation
   * from the reference cell to those cells will go to zero, affecting the error
   * constants of the finite element estimates.
   *
   * Those cells have a corner with an angle that is very close to 180 degrees,
   * i.e., an angle fraction very close to one.
   *
   * The same code, adding a call to regularize_corner_cells:
   * @code
   * const SphericalManifold<dim> m0;
   * Triangulation<dim> tria;
   * GridGenerator::hyper_cube(tria,-1,1);
   * tria.set_all_manifold_ids_on_boundary(0);
   * tria.set_manifold(0, m0);
   * GridTools::regularize_corner_cells(tria);
   * tria.refine_global(2);
   * @endcode
   * generates a mesh that has a much better behavior w.r.t. the jacobian of
   * the Mapping:
   *
   * <p ALIGN="center">
   * @image html regularize_mesh_02.png
   * </p>
   *
   * This mesh is very similar to the one obtained by GridGenerator::hyper_ball.
   * However, using GridTools::regularize_corner_cells one has the freedom to
   * choose when to apply the regularization, i.e., one could in principle first
   * refine a few times, and then call the regularize_corner_cells function:
   *
   * @code
   * const SphericalManifold<dim> m0;
   * Triangulation<dim> tria;
   * GridGenerator::hyper_cube(tria,-1,1);
   * tria.set_all_manifold_ids_on_boundary(0);
   * tria.set_manifold(0, m0);
   * tria.refine_global(2);
   * GridTools::regularize_corner_cells(tria);
   * tria.refine_global(1);
   * @endcode
   *
   * This generates the following mesh:
   *
   * <p ALIGN="center">
   * @image html regularize_mesh_03.png
   * </p>
   *
   * The function is currently implemented only for dim = 2 and
   * will throw an exception if called with dim = 3.
   *
   * @param[in,out] tria Triangulation to regularize.
   *
   * @param[in] limit_angle_fraction Maximum ratio of angle or solid
   * angle that is allowed for a corner element in the mesh.
   */
  template <int dim, int spacedim>
  void
  regularize_corner_cells(Triangulation<dim, spacedim> &tria,
                          const double limit_angle_fraction = .75);

  /*@}*/
  /**
   * @name Finding cells and vertices of a triangulation
   */
  /*@{*/

  /**
   * Given a Triangulation's @p cache and a list of @p points, call
   * find_active_cell_around_point() on each element of @p points, and return
   * @p cells, reference positions @p qpoints, and a mapping @p maps from local
   * to global indices into @p points .
   *
   * @param[in] cache The triangulation's GridTools::Cache .
   * @param[in] points The point's vector.
   * @param[in] cell_hint (optional) A cell iterator for a cell which likely
   * contains the first point of @p points.
   *
   * @return A tuple containing the following information:
   *  - @p cells : A vector of all the cells containing at least one of
   *   the @p points .
   *  - @p qpoints : A vector of vectors of points. @p qpoints[i] contains
   *   the reference positions of all points that fall within the cell @p cells[i] .
   *  - @p indices : A vector of vectors of integers, containing the mapping between
   *   local numbering in @p qpoints , and global index in @p points .
   *
   * If @p points[a] and @p points[b] are the only two points that fall in @p cells[c],
   * then @p qpoints[c][0] and @p qpoints[c][1] are the reference positions of
   * @p points[a] and @p points[b] in @p cells[c], and @p indices[c][0] = a,
   * @p indices[c][1] = b. The function
   * Mapping::transform_unit_to_real(qpoints[c][0])
   * returns @p points[a].
   *
   * The algorithm builds an rtree of @p points to sort them spatially, before
   * attempting to call find_active_cell_around_point().
   *
   * @note This function is not implemented for the codimension one case (<tt>spacedim != dim</tt>).
   *
   * @note If a point is not found inside the mesh, or is lying inside an
   * artificial cell of a parallel::TriangulationBase, the point is silently
   * ignored. If you want to infer for which points the search failed, use the
   * function compute_point_locations_try_all() that also returns a vector of
   * indices indicating the points for which the search failed.
   *
   * @note The actual return type of this function, i.e., the type referenced
   * above as @p return_type, is
   * @code
   * std::tuple<
   *   std::vector<
   *     typename Triangulation<dim, spacedim>::active_cell_iterator>,
   *   std::vector<std::vector<Point<dim>>>,
   *   std::vector<std::vector<unsigned int>>>
   * @endcode
   * The type is abbreviated in the online documentation to improve readability
   * of this page.
   *
   * @note This function optimizes the search by making use of
   * GridTools::Cache::get_cell_bounding_boxes_rtree(), which either returns
   * a cached rtree or builds and stores one. Building an rtree might hinder
   * the performance if the function is called only once on few points.
   */
  template <int dim, int spacedim>
#ifndef DOXYGEN
  std::tuple<
    std::vector<typename Triangulation<dim, spacedim>::active_cell_iterator>,
    std::vector<std::vector<Point<dim>>>,
    std::vector<std::vector<unsigned int>>>
#else
  return_type
#endif
  compute_point_locations(
    const Cache<dim, spacedim> &        cache,
    const std::vector<Point<spacedim>> &points,
    const typename Triangulation<dim, spacedim>::active_cell_iterator
      &cell_hint =
        typename Triangulation<dim, spacedim>::active_cell_iterator());

  /**
   * This function is similar to GridTools::compute_point_locations(),
   * but while compute_point_locations() silently ignores all points for which
   * find_active_cell_around_point() fails, this function also returns a
   * vector containing the indices of the points for which
   * find_active_cell_around_point() failed.
   *
   * @return A tuple containing four elements; the first three
   * are documented in GridTools::compute_point_locations().
   * The last element of the @p return_type contains the
   * indices of points which are neither found inside the mesh
   * nor lie in artificial cells. The @p return_type equals the
   * following tuple type:
   * @code
   *   std::tuple<
   *     std::vector<
   *        typename Triangulation<dim,spacedim>::active_cell_iterator>,
   *     std::vector<std::vector<Point<dim>>>,
   *     std::vector<std::vector<unsigned int>>,
   *     std::vector<unsigned int>
   *   >
   * @endcode
   *
   * @note This function is not implemented for the codimension one case (<tt>spacedim != dim</tt>).
   *
   * @note This function optimizes the search by making use of
   * GridTools::Cache::get_cell_bounding_boxes_rtree(), which either returns
   * a cached rtree or builds and stores one. Building an rtree might hinder
   * the performance if the function is called only once on few points.
   *
   * For a more detailed documentation see
   * GridTools::compute_point_locations().
   */
  template <int dim, int spacedim>
#ifndef DOXYGEN
  std::tuple<
    std::vector<typename Triangulation<dim, spacedim>::active_cell_iterator>,
    std::vector<std::vector<Point<dim>>>,
    std::vector<std::vector<unsigned int>>,
    std::vector<unsigned int>>
#else
  return_type
#endif
  compute_point_locations_try_all(
    const Cache<dim, spacedim> &        cache,
    const std::vector<Point<spacedim>> &points,
    const typename Triangulation<dim, spacedim>::active_cell_iterator
      &cell_hint =
        typename Triangulation<dim, spacedim>::active_cell_iterator());

  /**
   * Given a @p cache and a list of
   * @p local_points for each process, find the points lying on the locally
   * owned part of the mesh and compute the quadrature rules for them.
   * Distributed compute point locations is a function similar to
   * GridTools::compute_point_locations but working for
   * parallel::TriangulationBase objects and, unlike its serial version, also
   * for a distributed triangulation (see parallel::distributed::Triangulation).
   *
   * @param[in] cache a GridTools::Cache object
   * @param[in] local_points the array of points owned by the current process.
   * Every process can have a different array of points which can be empty and
   * not contained within the locally owned part of the triangulation
   * @param[in] global_bboxes a vector of vectors of bounding boxes; it
   * describes the locally owned part of the mesh for each process. The bounding
   * boxes describing which part of the mesh is locally owned by process with
   * rank rk are contained in global_bboxes[rk]. The local description can be
   * obtained from GridTools::compute_mesh_predicate_bounding_box; then the
   * global one can be obtained using either
   * GridTools::exchange_local_bounding_boxes or Utilities::MPI::all_gather
   * @param[in] tolerance Tolerance in terms of unit cell coordinates. Depending
   *   on the problem, it might be necessary to adjust the tolerance in order
   *   to be able to identify a cell. Floating
   *   point arithmetic implies that a point will, in general, not lie exactly
   *   on a vertex, edge, or face. In either case, it is not predictable which
   *   of the cells adjacent to a vertex or an edge/face this function returns.
   *   Consequently, algorithms that call this function need to take into
   *   account that the returned cell will only contain the point approximately.
   * @return A tuple containing the quadrature information
   *
   * The elements of the output tuple are:
   * - cells : a vector of all cells containing at least one point.
   * - qpoints : a vector of vector of points; containing in @p qpoints[i]
   *   the reference positions of all points that fall within the cell @p cells[i] .
   * - maps : a vector of vector of integers, containing the mapping between
   *  the numbering in qpoints (previous element of the tuple), and the vector
   *  of local points of the process owning the points.
   * - points : a vector of vector of points. @p points[i][j] is the point in the
   *  real space corresponding.
   *  to @p qpoints[i][j] . Notice @p points are the points lying on the locally
   *  owned part of the mesh; thus these can be either copies of @p local_points
   *  or points received from other processes i.e. local_points for other
   * processes
   * - owners : a vector of vectors; @p owners[i][j] contains the rank of
   *  the process owning the point[i][j] (previous element of the tuple).
   *
   * The function uses the triangulation's mpi communicator: for this reason it
   * throws an assert error if the Triangulation is not derived from
   * parallel::TriangulationBase .
   *
   * In a serial execution the first three elements of the tuple are the same
   * as in GridTools::compute_point_locations .
   *
   * Note: this function is a collective operation.
   *
   * @note The actual return type of this function, i.e., the type referenced
   * above as @p return_type, is
   * @code
   * std::tuple<
   *   std::vector<
   *     typename Triangulation<dim, spacedim>::active_cell_iterator>,
   *   std::vector<std::vector<Point<dim>>>,
   *   std::vector<std::vector<unsigned int>>,
   *   std::vector<std::vector<Point<spacedim>>>,
   *   std::vector<std::vector<unsigned int>>>
   * @endcode
   * The type is abbreviated in the online documentation to improve readability
   * of this page.
   */
  template <int dim, int spacedim>
#ifndef DOXYGEN
  std::tuple<
    std::vector<typename Triangulation<dim, spacedim>::active_cell_iterator>,
    std::vector<std::vector<Point<dim>>>,
    std::vector<std::vector<unsigned int>>,
    std::vector<std::vector<Point<spacedim>>>,
    std::vector<std::vector<unsigned int>>>
#else
  return_type
#endif
  distributed_compute_point_locations(
    const GridTools::Cache<dim, spacedim> &                cache,
    const std::vector<Point<spacedim>> &                   local_points,
    const std::vector<std::vector<BoundingBox<spacedim>>> &global_bboxes,
    const double                                           tolerance = 1e-10);

  namespace internal
  {
    /**
     * Data structure returned by
     * GridTools::internal::distributed_compute_point_locations(). It provides
     * information to perform GridTools::distributed_compute_point_locations()
     * and to set up the communication pattern within
     * Utilities::MPI::RemotePointEvaluation::reinit().
     *
     * @note The name of the fields are chosen with
     *   Utilities::MPI::RemotePointEvaluation in mind. Here, quantities are
     *   computed at specified arbitrary positioned points (and even on remote
     *   processes in the MPI universe) cell by cell and these values are sent
     *   to requesting processes, which receive the result and resort the
     *   result according to the points.
     */
    template <int dim, int spacedim>
    struct DistributedComputePointLocationsInternal
    {
      /**
       * Information of each point on sending/evaluation side. The elements of
       * the tuple are as follows: 0) cell level and index, 1) rank of the
       * owning process, 2) local index of the owning process, 3) reference
       * position, 4) real position, 5) permutation index within a send buffer.
       */
      std::vector<std::tuple<std::pair<int, int>,
                             unsigned int,
                             unsigned int,
                             Point<dim>,
                             Point<spacedim>,
                             unsigned int>>
        send_components;

      /**
       * Ranks to send to.
       */
      std::vector<unsigned int> send_ranks;

      /**
       * Pointers of ranges within a send buffer to be sent to the ranks
       * specified by send_ranks. The size of the send buffer is given
       * by send_ptrs.back().
       */
      std::vector<unsigned int> send_ptrs;

      /**
       * Information of each received data value. The elements of the tuple are
       * as follows: 0) rank of sender, 1) local index, 2) enumeration index.
       *
       * @note The vector is sorted according to 1), 0), 2).
       *
       * @note To each point multiple data values might be associated to. This
       *   might be the case if a point coincides with a geometric entity (e.g.,
       *   vertex) that is shared by multiple cells.
       */
      std::vector<std::tuple<unsigned int, unsigned int, unsigned int>>
        recv_components;

      /**
       * Ranks from where data is received.
       */
      std::vector<unsigned int> recv_ranks;

      /**
       * Pointers of ranges within a receive buffer that are filled by ranks
       * specified by recv_ranks. The size of the receive buffer is given by
       * recv_ptrs.back().
       */
      std::vector<unsigned int> recv_ptrs;
    };

    /**
     * A function that fills DistributedComputePointLocationsInternal.
     * If the input argument @p perform_handshake is set to false only
     * the fields needed by
     * GridTools::internal::distributed_compute_point_locations() are filled.
     * If the input argument is set to true additional data structures are
     * set up to be able to setup the communication pattern within
     * Utilities::MPI::RemotePointEvaluation::reinit().
     */
    template <int dim, int spacedim>
    DistributedComputePointLocationsInternal<dim, spacedim>
    distributed_compute_point_locations(
      const GridTools::Cache<dim, spacedim> &                cache,
      const std::vector<Point<spacedim>> &                   points,
      const std::vector<std::vector<BoundingBox<spacedim>>> &global_bboxes,
      const std::vector<bool> &                              marked_vertices,
      const double                                           tolerance,
      const bool                                             perform_handshake,
      const bool enforce_unique_mapping = false);

  } // namespace internal

  /**
   * Return a map `vertex index -> Point<spacedim>` containing the used
   * vertices of the given `container`. The key of the returned map (i.e.,
   * the first element of the pair above) is the global index in the
   * triangulation, whereas the value of each pair is the physical
   * location of the corresponding vertex. The used vertices are obtained by
   * looping over all cells,
   * and querying for each cell where its vertices are through the (optional)
   * `mapping` argument.
   *
   * In serial Triangulation objects and parallel::shared::Triangulation
   * objects, the size of the returned map
   * equals Triangulation::n_used_vertices() (not Triangulation::n_vertices()).
   * Note that in parallel::distributed::Triangulation objects, only vertices in
   * locally owned cells and ghost cells are returned, as for all other vertices
   * their real location might not be known (e.g. for distributed computations
   * using MappingQEulerian).
   *
   * If you use the default `mapping`, the returned map satisfies the following
   * equality:
   *
   * @code
   * const auto used_vertices = extract_used_vertices(tria);
   * auto all_vertices = tria.get_vertices();
   *
   * for(const auto &id_and_v : used_vertices)
   *   all_vertices[id_and_v.first] == id_and_v.second; // true
   * @endcode
   *
   * Notice that the above is not satisfied for mappings that change the
   * location of vertices, like MappingQEulerian.
   *
   * @ref ConceptMeshType "MeshType concept".
   * @param container The container to extract vertices from.
   * @param mapping The mapping to use to compute the points locations.
   */
  template <int dim, int spacedim>
  std::map<unsigned int, Point<spacedim>>
  extract_used_vertices(
    const Triangulation<dim, spacedim> &container,
    const Mapping<dim, spacedim> &      mapping =
      (ReferenceCells::get_hypercube<dim>()
#ifndef _MSC_VER
         .template get_default_linear_mapping<dim, spacedim>()
#else
         .ReferenceCell::get_default_linear_mapping<dim, spacedim>()
#endif
         ));

  /**
   * Find and return the index of the closest vertex to a given point in the
   * map of vertices passed as the first argument.
   *
   * @param vertices A map of index->vertex, as returned by
   *        GridTools::extract_used_vertices().
   * @param p The target point.
   * @return The index of the vertex that is closest to the target point `p`.
   */
  template <int spacedim>
  unsigned int
  find_closest_vertex(const std::map<unsigned int, Point<spacedim>> &vertices,
                      const Point<spacedim> &                        p);

  /**
   * Find and return the index of the used vertex (or marked vertex) in a
   * given mesh that is located closest to a given point.
   *
   * This function uses the locations of vertices as stored in the
   * triangulation. This is usually sufficient, unless you are using a Mapping
   * that moves the vertices around (for example, MappingQEulerian). In this
   * case, you should call the function with the same name and with an
   * additional Mapping argument.
   *
   * @param mesh A variable of a type that satisfies the requirements of the
   * @ref ConceptMeshType "MeshType concept".
   * @param p The point for which we want to find the closest vertex.
   * @param marked_vertices An array of bools indicating which
   * vertices of @p mesh will be considered within the search
   * as the potentially closest vertex. On receiving a non-empty
   * @p marked_vertices, the function will
   * only search among @p marked_vertices for the closest vertex.
   * The size of this array should be equal to the value returned by
   * Triangulation::n_vertices() for the triangulation underlying the given mesh
   * (as opposed to the value returned by Triangulation::n_used_vertices()).
   * @return The index of the closest vertex found.
   */
  template <int dim, template <int, int> class MeshType, int spacedim>
  unsigned int
  find_closest_vertex(const MeshType<dim, spacedim> &mesh,
                      const Point<spacedim> &        p,
                      const std::vector<bool> &      marked_vertices = {});

  /**
   * Find and return the index of the used vertex (or marked vertex) in a
   * given mesh that is located closest to a given point. Use the given
   * mapping to compute the actual location of the vertices.
   *
   * If the Mapping does not modify the position of the mesh vertices (like,
   * for example, MappingQEulerian does), then this function is equivalent to
   * the one with the same name, and without the `mapping` argument.
   *
   * @param mapping A mapping used to compute the vertex locations
   * @param mesh A variable of a type that satisfies the requirements of the
   * @ref ConceptMeshType "MeshType concept".
   * @param p The point for which we want to find the closest vertex.
   * @param marked_vertices An array of bools indicating which
   * vertices of @p mesh will be considered within the search
   * as the potentially closest vertex. On receiving a non-empty
   * @p marked_vertices, the function will
   * only search among @p marked_vertices for the closest vertex.
   * The size of this array should be equal to the value returned by
   * Triangulation::n_vertices() for the triangulation underlying the given mesh
   * (as opposed to the value returned by Triangulation::n_used_vertices()).
   * @return The index of the closest vertex found.
   */
  template <int dim, template <int, int> class MeshType, int spacedim>
  unsigned int
  find_closest_vertex(const Mapping<dim, spacedim> & mapping,
                      const MeshType<dim, spacedim> &mesh,
                      const Point<spacedim> &        p,
                      const std::vector<bool> &      marked_vertices = {});


  /**
   * Find and return a vector of iterators to active cells that surround a
   * given vertex with index @p vertex_index.
   *
   * For locally refined grids, the vertex itself might not be a vertex of all
   * adjacent cells that are returned. However, it will always be either a
   * vertex of a cell or be a hanging node located on a face or an edge of it.
   *
   * @param container A variable of a type that satisfies the requirements of
   * the
   * @ref ConceptMeshType "MeshType concept".
   * @param vertex_index The index of the vertex for which we try to find
   * adjacent cells.
   * @return A vector of cells that lie adjacent to the given vertex.
   *
   * @note It isn't entirely clear at this time whether the function does the
   * right thing with anisotropically refined meshes. It needs to be checked
   * for this case.
   */
  template <int dim, template <int, int> class MeshType, int spacedim>
#ifndef _MSC_VER
  std::vector<typename MeshType<dim, spacedim>::active_cell_iterator>
#else
  std::vector<
    typename dealii::internal::
      ActiveCellIterator<dim, spacedim, MeshType<dim, spacedim>>::type>
#endif
  find_cells_adjacent_to_vertex(const MeshType<dim, spacedim> &container,
                                const unsigned int             vertex_index);

  /**
   * Find an active non-artificial cell that surrounds a given point @p p. The return type
   * is a pair of an iterator to the active cell along with the unit cell
   * coordinates of the point.
   *
   * The algorithm used in this function proceeds by first looking for the
   * vertex located closest to the given point, see
   * GridTools::find_closest_vertex(). Secondly, all adjacent cells to this
   * vertex are found in the mesh, see
   * GridTools::find_cells_adjacent_to_vertex(). Lastly, for each of these
   * cells, the function tests whether the point is inside. This check is
   * performed using the given @p mapping argument to determine whether cells
   * have straight or curved boundaries.
   *
   * If a point lies on the boundary of two or more cells, then the algorithm
   * tries to identify the cell that is of highest refinement level.
   *
   * If the point requested does not lie in a locally-owned or ghost cell,
   * then this function will return the (invalid) MeshType<dim, spacedim>::end()
   * iterator. This case can be handled similarly to the various `std::find()`
   * and `std::lower_bound()` functions.
   *
   * @param mapping The mapping used to determine whether the given point is
   *   inside a given cell.
   * @param mesh A variable of a type that satisfies the requirements of the
   *   @ref ConceptMeshType "MeshType concept".
   * @param p The point for which we want to find the surrounding cell.
   * @param marked_vertices An array of `bool`s indicating whether an
   *   entry in the vertex array should be considered
   *   (and the others must be ignored) as the potentially
   *   closest vertex to the specified point. On specifying a non-default
   *   @p marked_vertices, find_closest_vertex() would
   *   only search among @p marked_vertices for the closest vertex.
   *   The size of this array should be equal to n_vertices() of the
   *   triangulation (as opposed to n_used_vertices() ). The motivation of using
   *   @p marked_vertices is to cut down the search space of vertices if one has
   *   a priori knowledge of a collection of vertices that the point of interest
   *   may be close to.
   * @param tolerance Tolerance in terms of unit cell coordinates. Depending
   *   on the problem, it might be necessary to adjust the tolerance in order
   *   to be able to identify a cell. Floating
   *   point arithmetic implies that a point will, in general, not lie exactly
   *   on a vertex, edge, or face. In either case, it is not predictable which
   *   of the cells adjacent to a vertex or an edge/face this function returns.
   *   Consequently, algorithms that call this function need to take into
   *   account that the returned cell will only contain the point approximately.
   *
   * @return A pair of an iterators into the mesh that points to the
   * surrounding cell, and of the unit cell coordinates of that point. This
   * local position might be located slightly outside an actual unit cell,
   * due to numerical roundoff. Therefore, the point returned by this function
   * should be projected onto the unit cell, using
   * GeometryInfo::project_to_unit_cell().  This is not automatically performed
   * by the algorithm. The returned cell can be a locally-owned cell or a
   * ghost cell (but not an artificial cell). The returned cell might be a
   * ghost cell even if the given point is a vertex of a locally owned cell.
   * The reason behind is that this is the only way to guarantee that all
   * processors that participate in a parallel triangulation will agree which
   * cell contains a point. For example, if two processors come together
   * at one vertex and the function is called with this vertex, then one
   * processor will return a locally owned cell and the other one a ghost cell.
   */
  template <int dim, template <int, int> class MeshType, int spacedim>
#ifndef _MSC_VER
  std::pair<typename MeshType<dim, spacedim>::active_cell_iterator, Point<dim>>
#else
  std::pair<typename dealii::internal::
              ActiveCellIterator<dim, spacedim, MeshType<dim, spacedim>>::type,
            Point<dim>>
#endif
  find_active_cell_around_point(const Mapping<dim, spacedim> & mapping,
                                const MeshType<dim, spacedim> &mesh,
                                const Point<spacedim> &        p,
                                const std::vector<bool> &marked_vertices = {},
                                const double             tolerance = 1.e-10);

  /**
   * A version of the above function that assumes straight boundaries and
   * as a consequence simply calls the above function using MappingQ1 for
   * the mapping argument.
   *
   * @return An iterator into the mesh that points to the surrounding cell.
   */
  template <int dim, template <int, int> class MeshType, int spacedim>
#ifndef _MSC_VER
  typename MeshType<dim, spacedim>::active_cell_iterator
#else
  typename dealii::internal::
    ActiveCellIterator<dim, spacedim, MeshType<dim, spacedim>>::type
#endif
  find_active_cell_around_point(const MeshType<dim, spacedim> &mesh,
                                const Point<spacedim> &        p,
                                const std::vector<bool> &marked_vertices = {},
                                const double             tolerance = 1.e-10);

  /**
   * Another version where we use that mapping on a given
   * cell that corresponds to the active finite element index of that cell.
   * This is obviously only useful for hp-problems, since the active finite
   * element index for all other DoF handlers is always zero.
   */
  template <int dim, int spacedim>
  std::pair<typename DoFHandler<dim, spacedim>::active_cell_iterator,
            Point<dim>>
  find_active_cell_around_point(
    const hp::MappingCollection<dim, spacedim> &mapping,
    const DoFHandler<dim, spacedim> &           mesh,
    const Point<spacedim> &                     p,
    const double                                tolerance = 1.e-10);

  /**
   * Finding an active non-artificial cell around a point can be very expensive
   * in terms of computational costs. This function aims at providing a fast
   * version of the above functions by using a space-tree to speed up the
   * geometry search.
   *
   * @param cache Object with information about the space-tree of a triangulation,
   * see GridTools::Cache.
   * @param p The point for which we want to find the surrounding cell.
   * @param cell_hint Gives a hint for the geometry search, which is beneficial
   * if a-priori knowledge is available regarding the cell on which the point
   * may likely be located. A typical use case would be that this search has
   * to be done for an array of points that are close to each other and where
   * the adjacent cell of the previous point is a good hint for the next point
   * in the array.
   * @param marked_vertices See above.
   * @param tolerance See above.
   *
   *
   * The following code example shows how to use this function:
   *
   * @code
   * GridTools::Cache<dim, dim> cache(triangulation, mapping);
   * auto cell_hint = typename Triangulation<dim, dim>::active_cell_iterator();
   * std::vector<bool> marked_vertices = {};
   * double tolerance = 1.e-10;
   *
   * std::vector<Point<dim>> points; // a vector of many points
   * ...
   *
   * for(auto p : points)
   * {
   *   auto cell_and_ref_point = GridTools::find_active_cell_around_point(
   *     cache, p, cell_hint, marked_vertices, tolerance);
   *
   *   if (cell_and_ref_point.first != triangulation.end())
   *     {
   *      // use current cell as hint for the next point
   *      cell_hint = cell_and_ref_point.first;
   *      // do something with cell_and_ref_point
   *      ...
   *   }
   *  else
   *    {
   *       // The function did not find a locally owned or ghost cell in which
   *       // the point is located. We ought to handle this somehow here.
   *    }
   *   ...
   * }
   * @endcode
   */
  template <int dim, int spacedim>
  std::pair<typename Triangulation<dim, spacedim>::active_cell_iterator,
            Point<dim>>
  find_active_cell_around_point(
    const Cache<dim, spacedim> &cache,
    const Point<spacedim> &     p,
    const typename Triangulation<dim, spacedim>::active_cell_iterator &
      cell_hint = typename Triangulation<dim, spacedim>::active_cell_iterator(),
    const std::vector<bool> &marked_vertices = {},
    const double             tolerance       = 1.e-10);

  /**
   * A version of the previous function that exploits an already existing
   * map between vertices and cells (constructed using the function
   * GridTools::vertex_to_cell_map()), a map of vertex_to_cell_centers (obtained
   * through GridTools::vertex_to_cell_centers_directions()), and
   * optionally an RTree constructed from the used vertices of the
   * Triangulation.
   *
   * @note All of these structures can be queried from a
   * GridTools::Cache object. Note, however, that in this case MeshType
   * has to be Triangulation, so that it might be more appropriate to directly
   * call the function above with argument `cache` in this case.
   */
  template <int dim, template <int, int> class MeshType, int spacedim>
#ifndef _MSC_VER
  std::pair<typename MeshType<dim, spacedim>::active_cell_iterator, Point<dim>>
#else
  std::pair<typename dealii::internal::
              ActiveCellIterator<dim, spacedim, MeshType<dim, spacedim>>::type,
            Point<dim>>
#endif
  find_active_cell_around_point(
    const Mapping<dim, spacedim> & mapping,
    const MeshType<dim, spacedim> &mesh,
    const Point<spacedim> &        p,
    const std::vector<
      std::set<typename MeshType<dim, spacedim>::active_cell_iterator>>
      &                                                  vertex_to_cell_map,
    const std::vector<std::vector<Tensor<1, spacedim>>> &vertex_to_cell_centers,
    const typename MeshType<dim, spacedim>::active_cell_iterator &cell_hint =
      typename MeshType<dim, spacedim>::active_cell_iterator(),
    const std::vector<bool> &                              marked_vertices = {},
    const RTree<std::pair<Point<spacedim>, unsigned int>> &used_vertices_rtree =
      RTree<std::pair<Point<spacedim>, unsigned int>>{},
    const double tolerance = 1.e-10,
    const RTree<
      std::pair<BoundingBox<spacedim>,
                typename Triangulation<dim, spacedim>::active_cell_iterator>>
      *relevant_cell_bounding_boxes_rtree = nullptr);

  /**
   * As compared to the functions above, this function identifies all active
   * non-artificial cells around a point for a given tolerance level `tolerance`
   * in terms of unit coordinates. Given a first cell with reference coordinates
   * as parameter
   * @p first_cell, e.g. obtained by one of the functions above, all
   * corresponding neighboring cells with points in unit coordinates are also
   * identified.
   *
   * This function is useful e.g. for discontinuous function spaces where, for
   * the case the given point `p` lies on a vertex, edge or face, several
   * cells might hold independent values of the solution that get combined in
   * some way in a user code.
   *
   * This function is used as follows
   * @code
   *   auto first_pair = GridTools::find_active_cell_around_point(...);
   *   auto all_cells  = GridTools::find_all_active_cells_around_point(
   *   			   mapping, mesh, p, tolerance, first_pair);
   * @endcode
   */
  template <int dim, template <int, int> class MeshType, int spacedim>
#ifndef _MSC_VER
  std::vector<std::pair<typename MeshType<dim, spacedim>::active_cell_iterator,
                        Point<dim>>>
#else
  std::vector<std::pair<
    typename dealii::internal::
      ActiveCellIterator<dim, spacedim, MeshType<dim, spacedim>>::type,
    Point<dim>>>
#endif
  find_all_active_cells_around_point(
    const Mapping<dim, spacedim> & mapping,
    const MeshType<dim, spacedim> &mesh,
    const Point<spacedim> &        p,
    const double                   tolerance,
    const std::pair<typename MeshType<dim, spacedim>::active_cell_iterator,
                    Point<dim>> &  first_cell);

  /**
   * A variant of the previous function that internally calls one of the
   * functions find_active_cell_around_point() to obtain a first cell, and
   * subsequently adds all other active non-artificial cells by calling the
   * function find_all_active_cells_around_point() above.
   */
  template <int dim, template <int, int> class MeshType, int spacedim>
#ifndef _MSC_VER
  std::vector<std::pair<typename MeshType<dim, spacedim>::active_cell_iterator,
                        Point<dim>>>
#else
  std::vector<std::pair<
    typename dealii::internal::
      ActiveCellIterator<dim, spacedim, MeshType<dim, spacedim>>::type,
    Point<dim>>>
#endif
  find_all_active_cells_around_point(
    const Mapping<dim, spacedim> & mapping,
    const MeshType<dim, spacedim> &mesh,
    const Point<spacedim> &        p,
    const double                   tolerance       = 1e-10,
    const std::vector<bool> &      marked_vertices = {});

  /**
   * Return a list of all descendants of the given cell that are active. For
   * example, if the current cell is once refined but none of its children are
   * any further refined, then the returned list will contain all its
   * children.
   *
   * If the current cell is already active, then the returned list is empty
   * (because the cell has no children that may be active).
   *
   * @tparam MeshType A type that satisfies the requirements of the
   * @ref ConceptMeshType "MeshType concept".
   * @param cell An iterator pointing to a cell of the mesh.
   * @return A list of active descendants of the given cell
   *
   * @note Since in C++ the MeshType template argument can not be deduced from
   * a function call, you will have to specify it after the function name, as
   * for example in
   * @code
   *   GridTools::get_active_child_cells<DoFHandler<dim> > (cell)
   * @endcode
   */
  template <class MeshType>
  std::vector<typename MeshType::active_cell_iterator>
  get_active_child_cells(const typename MeshType::cell_iterator &cell);

  /**
   * Extract the active cells around a given cell @p cell and return them in
   * the vector @p active_neighbors. These neighbors are specifically the
   * <i>face</i> neighbors of a cell or, if that neighbor is further
   * refined, its active children that border on that face. On the other
   * hand, the neighbors returned do not include cells that lie, for
   * example, diagonally opposite to a vertex but are not face neighbors
   * themselves. (In 3d, it also does not include cells that are
   * adjacent to one of the edges of the current cell, but are not
   * face neighbors.)
   *
   * @tparam MeshType A type that satisfies the requirements of the
   * @ref ConceptMeshType "MeshType concept".
   * @param[in] cell An iterator pointing to a cell of the mesh.
   * @param[out] active_neighbors A list of active descendants of the given
   * cell
   *
   * @note Since in C++ the MeshType template argument can not be deduced from
   * a function call, you will have to specify it after the function name, as
   * for example in
   * @code
   *   GridTools::get_active_neighbors<DoFHandler<dim>>(cell, active_neighbors)
   * @endcode
   */
  template <class MeshType>
  void
  get_active_neighbors(
    const typename MeshType::active_cell_iterator &       cell,
    std::vector<typename MeshType::active_cell_iterator> &active_neighbors);

  /**
   * Extract and return the active cell layer around a subdomain (set of
   * active cells) in the @p mesh (i.e. those that share a common set of
   * vertices with the subdomain but are not a part of it). Here, the
   * "subdomain" consists of exactly all of those cells for which the @p
   * predicate returns @p true.
   *
   * An example of a custom predicate is one that checks for a given material
   * id
   * @code
   * template <int dim>
   * bool
   * pred_mat_id(const typename Triangulation<dim>::active_cell_iterator & cell)
   * {
   *   return cell->material_id() ==  1;
   * }
   * @endcode
   * and we can then extract the layer of cells around this material with the
   * following call:
   * @code
   * GridTools::compute_active_cell_halo_layer(tria, pred_mat_id<dim>);
   * @endcode
   *
   * Predicates that are frequently useful can be found in namespace
   * IteratorFilters. For example, it is possible to extract a layer
   * of cells around all of those cells with a given material id,
   * @code
   * GridTools::compute_active_cell_halo_layer(
   *   tria, IteratorFilters::MaterialIdEqualTo(1, true));
   * @endcode
   * or around all cells with one of a set of active FE indices for a DoFHandler
   * with hp-capabilities
   * @code
   * GridTools::compute_active_cell_halo_layer(
   *   hp_dof_handler, IteratorFilters::ActiveFEIndexEqualTo({1,2}, true));
   * @endcode
   * Note that in the last two examples we ensure that the predicate returns
   * true only for locally owned cells. This means that the halo layer will
   * not contain any artificial cells.
   *
   * @tparam MeshType A type that satisfies the requirements of the
   * @ref ConceptMeshType "MeshType concept".
   * @param[in] mesh A mesh (i.e. objects of type Triangulation or DoFHandler).
   * @param[in] predicate A function  (or object of a type with an operator())
   * defining the subdomain around which the halo layer is to be extracted. It
   * is a function that takes in an active cell and returns a boolean.
   * @return A list of active cells sharing at least one common vertex with
   * the predicated subdomain.
   */
  template <class MeshType>
  std::vector<typename MeshType::active_cell_iterator>
  compute_active_cell_halo_layer(
    const MeshType &mesh,
    const std::function<bool(const typename MeshType::active_cell_iterator &)>
      &predicate);


  /**
   * Extract and return the cell layer around a subdomain (set of
   * cells) on a specified level of the @p mesh (i.e. those cells on
   * that level that share a common set of vertices with the subdomain
   * but are not a part of it). Here, the "subdomain" consists of exactly
   * all of those cells for which the @p predicate returns @p true.
   */
  template <class MeshType>
  std::vector<typename MeshType::cell_iterator>
  compute_cell_halo_layer_on_level(
    const MeshType &mesh,
    const std::function<bool(const typename MeshType::cell_iterator &)>
      &                predicate,
    const unsigned int level);


  /**
   * Extract and return ghost cells which are the active cell layer around all
   * locally owned cells. This is most relevant for
   * parallel::shared::Triangulation where it will return a subset of all
   * ghost cells on a processor, but for parallel::distributed::Triangulation
   * this will return all the ghost cells.
   *
   * @tparam MeshType A type that satisfies the requirements of the
   * @ref ConceptMeshType "MeshType concept".
   * @param[in] mesh A mesh (i.e. objects of type Triangulation or DoFHandler).
   * @return A list of ghost cells
   */
  template <class MeshType>
  std::vector<typename MeshType::active_cell_iterator>
  compute_ghost_cell_halo_layer(const MeshType &mesh);

  /**
   * Extract and return the set of active cells within a geometric distance of
   * @p layer_thickness around a subdomain (set of active cells) in the @p mesh.
   * Here, the "subdomain" consists of exactly all of
   * those cells for which the @p predicate returns @p true.
   *
   * The function first computes the cells that form the 'surface' of the
   * subdomain that consists of all of the active cells for which the predicate
   * is true. Using compute_bounding_box(), a bounding box is
   * computed for this subdomain and extended by @p layer_thickness. These
   * cells are called interior subdomain boundary cells.
   * The active cells with all of their vertices outside the extended
   * bounding box are ignored.
   * The cells that are inside the extended bounding box are then checked for
   * their proximity to the interior subdomain boundary cells. This implies
   * checking the distance between a pair of arbitrarily oriented cells,
   * which is not trivial in general. To simplify this, the algorithm checks
   * the distance between the two enclosing spheres of the cells.
   * This will definitely result in slightly more cells being marked but
   * also greatly simplifies the arithmetic complexity of the algorithm.
   *
   * @image html active_cell_layer_within_distance.png
   * The image shows a mesh generated by subdivided_hyper_rectangle(). The cells
   * are marked using three different colors. If the grey colored cells in the
   * image are the cells for which the predicate is true, then the function
   * compute_active_cell_layer_within_distance() will return a set of cell
   * iterators corresponding to the cells colored in red.
   * The red colored cells are the active cells that are within a given
   * distance to the grey colored cells.
   *
   * @tparam MeshType A type that satisfies the requirements of the
   * @ref ConceptMeshType "MeshType concept".
   * @param mesh A mesh (i.e. objects of type Triangulation or DoFHandler).
   * @param predicate A function  (or object of a type with an operator())
   * defining the subdomain around which the halo layer is to be extracted. It
   * is a function that takes in an active cell and returns a boolean.
   * @param layer_thickness specifies the geometric distance within
   * which the function searches for active cells from the predicate domain.
   * If the minimal distance between the enclosing sphere of the an
   * active cell and the enclosing sphere of any of the cells for which
   * the @p predicate returns @p true is less than @p layer_thickness,
   * then the active cell is an \a active_cell_within_distance.
   * @return A list of active cells within a given geometric distance
   * @p layer_thickness from the set of active cells for which the @p predicate
   * returns @p true.
   *
   * See compute_active_cell_halo_layer().
   */
  template <class MeshType>
  std::vector<typename MeshType::active_cell_iterator>
  compute_active_cell_layer_within_distance(
    const MeshType &mesh,
    const std::function<bool(const typename MeshType::active_cell_iterator &)>
      &          predicate,
    const double layer_thickness);

  /**
   * Extract and return a set of ghost cells which are within a
   * @p layer_thickness around all locally owned cells.
   * This is most relevant for parallel::shared::Triangulation
   * where it will return a subset of all ghost cells on a process, but for
   * parallel::distributed::Triangulation this will return all the ghost cells.
   * All the cells for the parallel::shared::Triangulation class that
   * are not owned by the current processor can be considered as ghost cells;
   * in particular, they do not only form a single layer of cells around the
   * locally owned ones.
   *
   * @tparam MeshType A type that satisfies the requirements of the
   * @ref ConceptMeshType "MeshType concept".
   * @param mesh A mesh (i.e. objects of type Triangulation or DoFHandler).
   * @param layer_thickness specifies the geometric distance within
   * which the function searches for active cells from the locally owned cells.
   * @return A subset of ghost cells within a given geometric distance of @p
   * layer_thickness from the locally owned cells of a current process.
   *
   * Also see compute_ghost_cell_halo_layer() and
   * compute_active_cell_layer_within_distance().
   */
  template <class MeshType>
  std::vector<typename MeshType::active_cell_iterator>
  compute_ghost_cell_layer_within_distance(const MeshType &mesh,
                                           const double    layer_thickness);

  /**
   * Compute and return a bounding box, defined through a pair of points
   * bottom left and top right, that surrounds a subdomain of the @p mesh.
   * Here, the "subdomain" consists of exactly all of those
   * active cells for which the @p predicate returns @p true.
   *
   * For a description of how @p predicate works,
   * see compute_active_cell_halo_layer().
   *
   * @note This function was written before the BoundingBox class was invented.
   *   Consequently, it returns a pair of points, rather than a BoundingBox
   * object as one may expect. However, BoundingBox has a conversion constructor
   * from pairs of points, so the result of this function can still be assigned
   * to a BoundingBox object.
   */
  template <class MeshType>
  std::pair<Point<MeshType::space_dimension>, Point<MeshType::space_dimension>>
  compute_bounding_box(
    const MeshType &mesh,
    const std::function<bool(const typename MeshType::active_cell_iterator &)>
      &predicate);

  /**
   * Compute a collection of bounding boxes so that all active cells for which
   * the given predicate is true, are completely enclosed in at least one of the
   * bounding boxes. Notice the cover is only guaranteed to contain all these
   * active cells but it's not necessarily exact i.e. it can include a bigger
   * area than their union.
   *
   * For each cell at a given refinement level containing active cells for which @p predicate is true,
   * the function creates a bounding box of its children for which @p predicate is true.
   *
   * This results in a cover of all active cells for which @p predicate is true; the parameters
   * @p allow_merge and @p max_boxes are used to reduce the number of cells at a computational cost and
   * covering a bigger n-dimensional volume.
   *
   * The parameters to control the algorithm are:
   * - @p predicate : the property of the cells to enclose e.g. IteratorFilters::LocallyOwnedCell .
   *  The predicate is tested only on active cells.
   * - @p refinement_level : it defines the level at which the initial bounding box are created. The refinement
   *  should be set to a coarse refinement level. A bounding box is created for
   * each active cell at coarser
   *  level than @p refinement_level; if @p refinement_level is higher than the number of levels of the
   *  triangulation an exception is thrown.
   * - @p allow_merge : This flag allows for box merging and, by default, is false. The algorithm has a cost of
   *  O(N^2) where N is the number of the bounding boxes created from the
   * refinement level; for this reason, if
   *  the flag is set to true, make sure to choose wisely a coarse enough @p refinement_level.
   * - @p max_boxes : the maximum number of bounding boxes to compute. If more are created the smaller ones are
   *  merged with neighbors. By default after merging the boxes which can be
   * expressed as a single one no more boxes are merged. See the
   * BoundingBox::get_neighbor_type () function for details.
   *  Notice only neighboring cells are merged (see the @p get_neighbor_type  function in bounding box class): if
   *  the target number of bounding boxes max_boxes can't be reached by merging
   * neighbors an exception is thrown
   *
   * The following image describes an example of the algorithm with @p refinement_level = 2, @p allow_merge = true
   * and @p max_boxes = 1. The cells with the property predicate are in red, the area of a bounding box is
   * slightly orange.
   * @image html bounding_box_predicate.png
   * - 1. In black we can see the cells of the current level.
   * - 2. For each cell containing the red area a bounding box is created: by
   * default these are returned.
   * - 3. Because @p allow_merge = true the number of bounding boxes is reduced while not changing the cover.
   *  If @p max_boxes was left as default or bigger than 1 these two boxes would be returned.
   * - 4. Because @p max_boxes = 1 the smallest bounding box is merged to the bigger one.
   * Notice it is important to choose the parameters wisely. For instance, @p allow_merge = false and
   * @p refinement_level = 1 returns the very same bounding box but with a
   * fraction of the computational cost.
   *
   * This function does not take into account the curvature of cells and thus it
   * is not suited for handling curved geometry: the mapping is assumed to be
   * linear.
   */
  template <class MeshType>
  std::vector<BoundingBox<MeshType::space_dimension>>
  compute_mesh_predicate_bounding_box(
    const MeshType &mesh,
    const std::function<bool(const typename MeshType::active_cell_iterator &)>
      &                predicate,
    const unsigned int refinement_level = 0,
    const bool         allow_merge      = false,
    const unsigned int max_boxes        = numbers::invalid_unsigned_int);

  /**
   * Given an array of points, use the global bounding box description obtained
   * using GridTools::compute_mesh_predicate_bounding_box to guess, for each of
   * them, which process might own it.
   *
   * @param[in] global_bboxes Vector of bounding boxes describing the portion of
   *  mesh with a property for each process.
   * @param[in] points Array of points to test.
   *
   * @return A tuple containing the following information:
   *  - A vector indicized with ranks of processes. For each rank it contains
   *   a vector of the indices of points it might own.
   *  - A map from the index <code>unsigned int</code> of the point in @p points
   *   to the rank of the owner.
   *  - A map from the index <code>unsigned int</code> of the point in @p points
   *   to the ranks of the guessed owners.
   *
   * @note The actual return type of this function, i.e., the type referenced
   * above as @p return_type, is
   * @code
   * std::tuple<std::vector<std::vector<unsigned int>>,
   *            std::map< unsigned int, unsigned int>,
   *            std::map< unsigned int, std::vector<unsigned int>>>
   * @endcode
   * The type is abbreviated in the online documentation to improve readability
   * of this page.
   */
  template <int spacedim>
#ifndef DOXYGEN
  std::tuple<std::vector<std::vector<unsigned int>>,
             std::map<unsigned int, unsigned int>,
             std::map<unsigned int, std::vector<unsigned int>>>
#else
  return_type
#endif
  guess_point_owner(
    const std::vector<std::vector<BoundingBox<spacedim>>> &global_bboxes,
    const std::vector<Point<spacedim>> &                   points);


  /**
   * Given a covering rtree (see GridTools::Cache::get_covering_rtree()), and an
   * array of points, find a superset of processes which, individually,
   * may own the cell containing the points.
   *
   * For further details see GridTools::guess_point_owner; here only
   * different input/output types are reported:
   *
   * @param[in] covering_rtree RTRee which enables us to identify which
   * process(es) in a parallel computation may own the cell that
   * surrounds a given point.
   *
   * @param[in] points A vector of points to consider.
   *
   * @return A tuple containing the following information:
   *  - A map indexed by processor ranks. For each rank it contains
   *   a vector of the indices of points it might own.
   *  - A map from the index <code>unsigned int</code> of the point in @p points
   *   to the rank of the owner; these are points for which a single possible
   *   owner was found.
   *  - A map from the index <code>unsigned int</code> of the point in @p points
   *   to the ranks of the guessed owners; these are points for which multiple
   *   possible owners were found.
   *
   * @note The actual return type of this function, i.e., the type referenced
   * above as @p return_type, is
   * @code
   * std::tuple<std::map<unsigned int, std::vector<unsigned int>>,
   *            std::map<unsigned int, unsigned int>,
   *            std::map<unsigned int, std::vector<unsigned int>>>
   * @endcode
   * The type is abbreviated in the online documentation to improve readability
   * of this page.
   */
  template <int spacedim>
#ifndef DOXYGEN
  std::tuple<std::map<unsigned int, std::vector<unsigned int>>,
             std::map<unsigned int, unsigned int>,
             std::map<unsigned int, std::vector<unsigned int>>>
#else
  return_type
#endif
  guess_point_owner(
    const RTree<std::pair<BoundingBox<spacedim>, unsigned int>> &covering_rtree,
    const std::vector<Point<spacedim>> &                         points);


  /**
   * Return the adjacent cells of all the vertices. If a vertex is also a
   * hanging node, the associated coarse cell is also returned. The vertices
   * are ordered by the vertex index. This is the number returned by the
   * function <code>cell-@>vertex_index()</code>. Notice that only the indices
   * marked in the array returned by
   * Triangulation<dim,spacedim>::get_used_vertices() are used.
   */
  template <int dim, int spacedim>
  std::vector<
    std::set<typename Triangulation<dim, spacedim>::active_cell_iterator>>
  vertex_to_cell_map(const Triangulation<dim, spacedim> &triangulation);

  /**
   * Return a vector of normalized tensors for each vertex-cell combination of
   * the output of GridTools::vertex_to_cell_map() (which is expected as input
   * parameter for this function). Each tensor represents a geometric vector
   * from the vertex to the respective cell center.
   *
   * An assertion will be thrown if the size of the input vector is not equal to
   * the number of vertices of the triangulation.
   *
   * result[v][c] is a unit Tensor for vertex index v, indicating the direction
   * of the center of the c-th cell with respect to the vertex v.
   */
  template <int dim, int spacedim>
  std::vector<std::vector<Tensor<1, spacedim>>>
  vertex_to_cell_centers_directions(
    const Triangulation<dim, spacedim> &mesh,
    const std::vector<
      std::set<typename Triangulation<dim, spacedim>::active_cell_iterator>>
      &vertex_to_cells);


  /**
   * Return the local vertex index of cell @p cell that is closest to
   * the given location @p position. The location of the vertices is extracted
   * from the (optional) @p mapping argument, to guarantee that the correct
   * answer is returned when the underlying mapping modifies the position of the
   * vertices.
   */
  template <int dim, int spacedim>
  unsigned int
  find_closest_vertex_of_cell(
    const typename Triangulation<dim, spacedim>::active_cell_iterator &cell,
    const Point<spacedim> &                                            position,
    const Mapping<dim, spacedim> &                                     mapping =
      (ReferenceCells::get_hypercube<dim>()
#ifndef _MSC_VER
         .template get_default_linear_mapping<dim, spacedim>()
#else
         .ReferenceCell::get_default_linear_mapping<dim, spacedim>()
#endif
         ));

  /**
   * Compute a globally unique index for each vertex and hanging node
   * associated with a locally owned active cell. The vertices of a ghost cell
   * that are hanging nodes of a locally owned cells have a global index.
   * However, the other vertices of the cells that do not <i>touch</i> an
   * active cell do not have a global index on this processor.
   *
   * The key of the map is the local index of the vertex and the value is the
   * global index. The indices need to be recomputed after refinement or
   * coarsening and may be different.
   */
  template <int dim, int spacedim>
  std::map<unsigned int, types::global_vertex_index>
  compute_local_to_global_vertex_index_map(
    const parallel::distributed::Triangulation<dim, spacedim> &triangulation);

  /**
   * Return the highest value among ratios between extents in each of the
   * coordinate directions of a @p cell. Moreover, return the dimension
   * relative to the highest elongation.
   *
   * @param[in] cell an iterator pointing to the cell.
   *
   * @return  A std::pair<unsigned int, double> such that the @p first value
   * is the dimension of the highest elongation and the @p second value is the
   * ratio among the dimensions of the @p cell.
   */
  template <int dim, int spacedim>
  std::pair<unsigned int, double>
  get_longest_direction(
    typename Triangulation<dim, spacedim>::active_cell_iterator cell);

  /*@}*/
  /**
   * @name Partitions and subdomains of triangulations
   */
  /*@{*/

  /**
   * Produce a sparsity pattern in which nonzero entries indicate that two
   * cells are connected via a common face. The diagonal entries of the
   * sparsity pattern are also set.
   *
   * The rows and columns refer to the cells as they are traversed in their
   * natural order using cell iterators.
   */
  template <int dim, int spacedim>
  void
  get_face_connectivity_of_cells(
    const Triangulation<dim, spacedim> &triangulation,
    DynamicSparsityPattern &            connectivity);

  /**
   * Produce a sparsity pattern in which nonzero entries indicate that two
   * cells are connected via a common vertex. The diagonal entries of the
   * sparsity pattern are also set.
   *
   * The rows and columns refer to the cells as they are traversed in their
   * natural order using cell iterators.
   */
  template <int dim, int spacedim>
  void
  get_vertex_connectivity_of_cells(
    const Triangulation<dim, spacedim> &triangulation,
    DynamicSparsityPattern &            connectivity);

  /**
   * Produce a sparsity pattern for a given level mesh in which nonzero entries
   * indicate that two cells are connected via a common vertex. The diagonal
   * entries of the sparsity pattern are also set.
   *
   * The rows and columns refer to the cells as they are traversed in their
   * natural order using cell iterators.
   */
  template <int dim, int spacedim>
  void
  get_vertex_connectivity_of_cells_on_level(
    const Triangulation<dim, spacedim> &triangulation,
    const unsigned int                  level,
    DynamicSparsityPattern &            connectivity);

  /**
   * Use graph partitioner to partition the active cells making up the entire
   * domain. After calling this function, the subdomain ids of all active cells
   * will have values
   * between zero and @p n_partitions-1. You can access the subdomain id of a cell by using
   * <tt>cell-@>subdomain_id()</tt>.
   *
   * Use the third argument to select between partitioning algorithms provided
   * by METIS or ZOLTAN. METIS is the default partitioner.
   *
   * If deal.II was not installed with ZOLTAN or METIS, this function will
   * generate an error
   * when corresponding partition method is chosen, unless @p n_partitions is one.
   * I.e., you can write a program so that it runs in the single-processor
   * single-partition case without packages installed, and only requires them
   * installed when multiple partitions are required.
   *
   * @note If the `weight` signal has been attached to the @p triangulation,
   * then this will be used and passed to the partitioner.
   */
  template <int dim, int spacedim>
  void
  partition_triangulation(const unsigned int               n_partitions,
                          Triangulation<dim, spacedim> &   triangulation,
                          const SparsityTools::Partitioner partitioner =
                            SparsityTools::Partitioner::metis);

  /**
   * This function performs the same operation as the one above, except that
   * it takes into consideration a specific set of @p cell_weights, which allow the
   * partitioner to balance the graph while taking into consideration the
   * computational effort expended on each cell.
   *
   * @note If the @p cell_weights vector is empty, then no weighting is taken
   * into consideration. If not then the size of this vector must equal to the
   * number of active cells in the triangulation.
   */
  template <int dim, int spacedim>
  void
  partition_triangulation(const unsigned int               n_partitions,
                          const std::vector<unsigned int> &cell_weights,
                          Triangulation<dim, spacedim> &   triangulation,
                          const SparsityTools::Partitioner partitioner =
                            SparsityTools::Partitioner::metis);

  /**
   * This function does the same as the previous one, i.e. it partitions a
   * triangulation using a partitioning algorithm into a number of subdomains
   * identified by the <code>cell-@>subdomain_id()</code> flag.
   *
   * The difference to the previous function is the second argument, a
   * sparsity pattern that represents the connectivity pattern between cells.
   *
   * While the function above builds it directly from the triangulation by
   * considering which cells neighbor each other, this function can take a
   * more refined connectivity graph. The sparsity pattern needs to be of size
   * $N\times N$, where $N$ is the number of active cells in the
   * triangulation. If the sparsity pattern contains an entry at position
   * $(i,j)$, then this means that cells $i$ and $j$ (in the order in which
   * they are traversed by active cell iterators) are to be considered
   * connected; partitioning algorithm will then try to partition the domain in
   * such a way that (i) the subdomains are of roughly equal size, and (ii) a
   * minimal number of connections are broken.
   *
   * This function is mainly useful in cases where connections between cells
   * exist that are not present in the triangulation alone (otherwise the
   * previous function would be the simpler one to use). Such connections may
   * include that certain parts of the boundary of a domain are coupled
   * through symmetric boundary conditions or integrals (e.g. friction contact
   * between the two sides of a crack in the domain), or if a numerical scheme
   * is used that not only connects immediate neighbors but a larger
   * neighborhood of cells (e.g. when solving integral equations).
   *
   * In addition, this function may be useful in cases where the default
   * sparsity pattern is not entirely sufficient. This can happen because the
   * default is to just consider face neighbors, not neighboring cells that
   * are connected by edges or vertices. While the latter couple when using
   * continuous finite elements, they are typically still closely connected in
   * the neighborship graph, and partitioning algorithm
   * will not usually cut important connections in this case. However, if there
   * are vertices in the mesh where many cells (many more than the common 4 or 6
   * in 2d and 3d, respectively) come together, then there will be a significant
   * number of cells that are connected across a vertex, but several degrees
   * removed in the connectivity graph built only using face neighbors. In a
   * case like this, partitioning algorithm may sometimes make bad decisions and
   * you may want to build your own connectivity graph.
   *
   * @note If the `weight` signal has been attached to the @p triangulation,
   * then this will be used and passed to the partitioner.
   */
  template <int dim, int spacedim>
  void
  partition_triangulation(const unsigned int            n_partitions,
                          const SparsityPattern &       cell_connection_graph,
                          Triangulation<dim, spacedim> &triangulation,
                          const SparsityTools::Partitioner partitioner =
                            SparsityTools::Partitioner::metis);

  /**
   * This function performs the same operation as the one above, except that
   * it takes into consideration a specific set of @p cell_weights, which allow the
   * partitioner to balance the graph while taking into consideration the
   * computational effort expended on each cell.
   *
   * @note If the @p cell_weights vector is empty, then no weighting is taken
   * into consideration. If not then the size of this vector must equal to the
   * number of active cells in the triangulation.
   */
  template <int dim, int spacedim>
  void
  partition_triangulation(const unsigned int               n_partitions,
                          const std::vector<unsigned int> &cell_weights,
                          const SparsityPattern &       cell_connection_graph,
                          Triangulation<dim, spacedim> &triangulation,
                          const SparsityTools::Partitioner partitioner =
                            SparsityTools::Partitioner::metis);

  /**
   * Generates a partitioning of the active cells making up the entire domain
   * using the same partitioning scheme as in the p4est library if the flag
   * @p group_siblings is set to true (default behavior of this function).
   * After calling this function, the subdomain ids of all active cells will
   * have values between zero and @p n_partitions-1. You can access the
   * subdomain id of a cell by using <tt>cell-@>subdomain_id()</tt>.
   *
   * @note If the flag @p group_siblings is set to false, children of a
   *       cell might be placed on different processors even though they are all
   *       active, which is an assumption made by p4est. By relaxing this, we
   *       can create partitions owning a single cell (also for refined
   *       meshes).
   */
  template <int dim, int spacedim>
  void
  partition_triangulation_zorder(const unsigned int            n_partitions,
                                 Triangulation<dim, spacedim> &triangulation,
                                 const bool group_siblings = true);

  /**
   * Partitions the cells of a multigrid hierarchy by assigning level subdomain
   * ids using the "youngest child" rule, that is, each cell in the hierarchy is
   * owned by the processor who owns its left most child in the forest, and
   * active cells have the same subdomain id and level subdomain id. You can
   * access the level subdomain id of a cell by using
   * <tt>cell-@>level_subdomain_id()</tt>.
   *
   * Note: This function assumes that the active cells have already been
   * partitioned.
   */
  template <int dim, int spacedim>
  void
  partition_multigrid_levels(Triangulation<dim, spacedim> &triangulation);

  /**
   * This function allows to ask for the owning subdomain of cells identified by
   * CellId objects that do not have to exist on the current process.
   *
   * @note This function has not been implemented yet for
   *   parallel::fullydistributed::Triangulation.
   */
  template <int dim, int spacedim>
  std::vector<types::subdomain_id>
  get_subdomain_association(const Triangulation<dim, spacedim> &triangulation,
                            const std::vector<CellId> &         cell_ids);

  /**
   * For each active cell, return in the output array to which subdomain (as
   * given by the <tt>cell->subdomain_id()</tt> function) it belongs. The
   * output array is supposed to have the right size already when calling this
   * function.
   *
   * This function returns the association of each cell with one subdomain. If
   * you are looking for the association of each @em DoF with a subdomain, use
   * the <tt>DoFTools::get_subdomain_association</tt> function.
   */
  template <int dim, int spacedim>
  void
  get_subdomain_association(const Triangulation<dim, spacedim> &triangulation,
                            std::vector<types::subdomain_id> &  subdomain);

  /**
   * Count how many cells are uniquely associated with the given @p subdomain
   * index.
   *
   * This function may return zero if there are no cells with the given @p
   * subdomain index. This can happen, for example, if you try to partition a
   * coarse mesh into more partitions (one for each processor) than there are
   * cells in the mesh.
   *
   * This function returns the number of cells associated with one subdomain.
   * If you are looking for the association of @em DoFs with this subdomain,
   * use the <tt>DoFTools::count_dofs_with_subdomain_association</tt>
   * function.
   */
  template <int dim, int spacedim>
  unsigned int
  count_cells_with_subdomain_association(
    const Triangulation<dim, spacedim> &triangulation,
    const types::subdomain_id           subdomain);

  /**
   * For a triangulation, return a mask that represents which of its vertices
   * are "owned" by the current process in the same way as we talk about
   * locally owned cells or degrees of freedom (see
   * @ref GlossLocallyOwnedCell
   * and
   * @ref GlossLocallyOwnedDof).
   * For the purpose of this function, we define a locally owned vertex as
   * follows: a vertex is owned by that processor with the smallest subdomain
   * id (which equals the MPI rank of that processor) among all owners of
   * cells adjacent to this vertex. In other words, vertices that are in the
   * interior of a partition of the triangulation are owned by the owner of
   * this partition; for vertices that lie on the boundary between two or more
   * partitions, the owner is the processor with the least subdomain_id among
   * all adjacent subdomains.
   *
   * For sequential triangulations (as opposed to, for example,
   * parallel::distributed::Triangulation), every user vertex is of course
   * owned by the current processor, i.e., the function returns
   * Triangulation::get_used_vertices(). For parallel triangulations, the
   * returned mask is a subset of what Triangulation::get_used_vertices()
   * returns.
   *
   * @param triangulation The triangulation of which the function evaluates
   * which vertices are locally owned.
   * @return The subset of vertices, as described above. The length of the
   * returned array equals Triangulation.n_vertices() and may, consequently,
   * be larger than Triangulation::n_used_vertices().
   */
  template <int dim, int spacedim>
  std::vector<bool>
  get_locally_owned_vertices(const Triangulation<dim, spacedim> &triangulation);

  /*@}*/
  /**
   * @name Comparing different meshes
   */
  /*@{*/

  /**
   * Given two meshes (i.e. objects of type Triangulation or DoFHandler) that
   * are based on the same coarse mesh, this function figures out a set of cells
   * that are matched between the two meshes and where at most one of the meshes
   * is more refined on this cell. In other words, it finds the smallest cells
   * that are common to both meshes, and that together completely cover the
   * domain.
   *
   * This function is useful, for example, in time-dependent or nonlinear
   * application, where one has to integrate a solution defined on one mesh
   * (e.g., the one from the previous time step or nonlinear iteration)
   * against the shape functions of another mesh (the next time step, the next
   * nonlinear iteration). If, for example, the new mesh is finer, then one
   * has to obtain the solution on the coarse mesh (mesh_1) and interpolate it
   * to the children of the corresponding cell of mesh_2. Conversely, if the
   * new mesh is coarser, one has to express the coarse cell shape function by
   * a linear combination of fine cell shape functions. In either case, one
   * needs to loop over the finest cells that are common to both
   * triangulations. This function returns a list of pairs of matching
   * iterators to cells in the two meshes that can be used to this end.
   *
   * Note that the list of these iterators is not necessarily ordered, and
   * does also not necessarily coincide with the order in which cells are
   * traversed in one, or both, of the meshes given as arguments.
   *
   * @tparam MeshType A type that satisfies the requirements of the
   * @ref ConceptMeshType "MeshType concept".
   *
   * @note This function can only be used with
   * parallel::distributed::Triangulation when both meshes use the same
   * Triangulation since, with a distributed Triangulation, not all cells are
   * stored locally, so the resulting list may not cover the entire domain.
   */
  template <typename MeshType>
  std::list<std::pair<typename MeshType::cell_iterator,
                      typename MeshType::cell_iterator>>
  get_finest_common_cells(const MeshType &mesh_1, const MeshType &mesh_2);

  /**
   * Return true if the two triangulations are based on the same coarse mesh.
   * This is determined by checking whether they have the same number of cells
   * on the coarsest level, and then checking that they have the same
   * vertices.
   *
   * The two meshes may have different refinement histories beyond the coarse
   * mesh.
   */
  template <int dim, int spacedim>
  bool
  have_same_coarse_mesh(const Triangulation<dim, spacedim> &mesh_1,
                        const Triangulation<dim, spacedim> &mesh_2);

  /**
   * The same function as above, but working on arguments of type DoFHandler.
   * This function is provided to allow calling have_same_coarse_mesh for all
   * types of containers representing triangulations or the classes built on
   * triangulations.
   *
   * @tparam MeshType A type that satisfies the requirements of the
   * @ref ConceptMeshType "MeshType concept".
   */
  template <typename MeshType>
  bool
  have_same_coarse_mesh(const MeshType &mesh_1, const MeshType &mesh_2);

  /*@}*/
  /**
   * @name Dealing with distorted cells
   */
  /*@{*/

  /**
   * Given a triangulation and a list of cells whose children have become
   * distorted as a result of mesh refinement, try to fix these cells up by
   * moving the center node around.
   *
   * The function returns a list of cells with distorted children that
   * couldn't be fixed up for whatever reason. The returned list is therefore
   * a subset of the input argument.
   *
   * For a definition of the concept of distorted cells, see the
   * @ref GlossDistorted "glossary entry".
   * The first argument passed to the current function is typically the
   * exception thrown by the Triangulation::execute_coarsening_and_refinement
   * function.
   */
  template <int dim, int spacedim>
  typename Triangulation<dim, spacedim>::DistortedCellList
  fix_up_distorted_child_cells(
    const typename Triangulation<dim, spacedim>::DistortedCellList
      &                           distorted_cells,
    Triangulation<dim, spacedim> &triangulation);



  /*@}*/
  /**
   * @name Extracting and creating patches of cells
   *
   * These functions extract and create patches of cells surrounding a single
   * cell, and creating triangulation out of them.
   */
  /*@{*/


  /**
   * This function returns a list of all the active neighbor cells of the
   * given, active cell.  Here, a neighbor is defined as one having at least
   * part of a face in common with the given cell, but not edge (in 3d) or
   * vertex neighbors (in 2d and 3d).
   *
   * The first element of the returned list is the cell provided as argument.
   * The remaining ones are neighbors: The function loops over all faces of
   * that given cell and checks if that face is not on the boundary of the
   * domain. Then, if the neighbor cell does not have any children (i.e., it
   * is either at the same refinement level as the current cell, or coarser)
   * then this neighbor cell is added to the list of cells. Otherwise, if the
   * neighbor cell is refined and therefore has children, then this function
   * loops over all subfaces of current face adds the neighbors behind these
   * sub-faces to the list to be returned.
   *
   * @tparam MeshType A type that satisfies the requirements of the
   * @ref ConceptMeshType "MeshType concept".
   * In C++, the compiler can not determine <code>MeshType</code> from the
   * function call. You need to specify it as an explicit template argument
   * following the function name.
   * @param[in] cell An iterator pointing to a cell of the mesh.
   * @return A list of active cells that form the patch around the given cell
   *
   * @note Patches are often used in defining error estimators that require
   * the solution of a local problem on the patch surrounding each of the
   * cells of the mesh. This also requires manipulating the degrees of freedom
   * associated with the cells of a patch. To this end, there are further
   * functions working on patches in namespace DoFTools.
   *
   * @note In the context of a parallel distributed computation, it only makes
   * sense to call this function on locally owned cells. This is because the
   * neighbors of locally owned cells are either locally owned themselves, or
   * ghost cells. For both, we know that these are in fact the real cells of
   * the complete, parallel triangulation. We can also query the degrees of
   * freedom on these.
   */
  template <class MeshType>
  std::vector<typename MeshType::active_cell_iterator>
  get_patch_around_cell(const typename MeshType::active_cell_iterator &cell);


  /**
   * This function takes a vector of active cells (hereafter named @p
   * patch_cells) as input argument, and returns a vector of their parent
   * cells with the coarsest common level of refinement. In other words, find
   * that set of cells living at the same refinement level so that all cells
   * in the input vector are children of the cells in the set, or are in the
   * set itself.
   *
   * @tparam Container In C++, the compiler can not determine the type of
   * <code>Container</code> from the function call. You need to specify it as
   * an explicit template argument following the function name. This type has
   * to satisfy the requirements of a mesh container (see
   * @ref ConceptMeshType).
   *
   * @param[in] patch_cells A vector of active cells for which this function
   * finds the parents at the coarsest common level. This vector of cells
   * typically results from calling the function
   * GridTools::get_patch_around_cell().
   * @return A list of cells with the coarsest common level of refinement of
   * the input cells.
   */
  template <class Container>
  std::vector<typename Container::cell_iterator>
  get_cells_at_coarsest_common_level(
    const std::vector<typename Container::active_cell_iterator> &patch_cells);

  /**
   * This function constructs a Triangulation (named @p local_triangulation)
   * from a given vector of active cells. This vector (which we think of the
   * cells corresponding to a "patch") contains active cells that are part of
   * an existing global Triangulation. The goal of this function is to build a
   * local Triangulation that contains only the active cells given in @p patch
   * (and potentially a minimum number of additional cells required to form a
   * valid Triangulation). The function also returns a map that allows to
   * identify the cells in the output Triangulation and corresponding cells in
   * the input list.
   *
   * The function copies the location of vertices of cells from the cells of the
   * source triangulation to the triangulation that is built from the list of
   * patch cells.  This adds support for triangulations which have been
   * perturbed or smoothed in some manner which makes the triangulation
   * deviate from the standard deal.II refinement strategy of placing new
   * vertices at midpoints of faces or edges.
   *
   * The operation implemented by this function is frequently used in the
   * definition of error estimators that need to solve "local" problems on
   * each cell and its neighbors. A similar construction is necessary in the
   * definition of the Clement interpolation operator in which one needs to
   * solve a local problem on all cells within the support of a shape
   * function. This function then builds a complete Triangulation from a list
   * of cells that make up such a patch; one can then later attach a
   * DoFHandler to such a Triangulation.
   *
   * If the list of input cells contains only cells at the same refinement
   * level, then the output Triangulation simply consists of a Triangulation
   * containing only exactly these patch cells. On the other hand, if the
   * input cells live on different refinement levels, i.e., the Triangulation
   * of which they are part is adaptively refined, then the construction of
   * the output Triangulation is not so simple because the coarsest level of a
   * Triangulation can not contain hanging nodes. Rather, we first have to
   * find the common refinement level of all input cells, along with their
   * common parents (see GridTools::get_cells_at_coarsest_common_level()),
   * build a Triangulation from those, and then adaptively refine it so that
   * the input cells all also exist in the output Triangulation.
   *
   * A consequence of this procedure is that the output Triangulation may
   * contain more active cells than the ones that exist in the input vector.
   * On the other hand, one typically wants to solve the local problem not on
   * the entire output Triangulation, but only on those cells of it that
   * correspond to cells in the input list.  In this case, a user typically
   * wants to assign degrees of freedom only on cells that are part of the
   * "patch", and somehow ignore those excessive cells. The current function
   * supports this common requirement by setting the user flag for the cells
   * in the output Triangulation that match with cells in the input list.
   * Cells which are not part of the original patch will not have their @p
   * user_flag set; we can then avoid assigning degrees of freedom using the
   * FE_Nothing<dim> element.
   *
   * @tparam Container In C++, the compiler can not determine the type of
   * <code>Container</code> from the function call. You need to specify it as
   * an explicit template argument following the function name. This type that
   * satisfies the requirements of a mesh container (see
   * @ref ConceptMeshType).
   *
   * @param[in] patch A vector of active cells from a common triangulation.
   * These cells may or may not all be at the same refinement level.
   * @param[out] local_triangulation A triangulation whose active cells
   * correspond to the given vector of active cells in @p patch.
   * @param[out] patch_to_global_tria_map A map between the local
   * triangulation which is built as explained above, and the cell iterators
   * in the input list.
   */
  template <class Container>
  void
  build_triangulation_from_patch(
    const std::vector<typename Container::active_cell_iterator> &patch,
    Triangulation<Container::dimension, Container::space_dimension>
      &local_triangulation,
    std::map<
      typename Triangulation<Container::dimension,
                             Container::space_dimension>::active_cell_iterator,
      typename Container::active_cell_iterator> &patch_to_global_tria_map);

  /**
   * This function runs through the degrees of freedom defined by the
   * DoFHandler and for each dof constructs a vector of
   * active_cell_iterators representing the cells of support of the associated
   * basis element at that degree of freedom. This function was originally
   * designed for the implementation of local projections, for instance the
   * Clement interpolant, in conjunction with other local patch functions like
   * GridTools::build_triangulation_from_patch.
   *
   * DoFHandler's built on top of Triangulation or
   * parallel:distributed::Triangulation are supported and handled
   * appropriately.
   *
   * The result is the patch of cells representing the support of the basis
   * element associated to the degree of freedom.  For instance using an FE_Q
   * finite element, we obtain the standard patch of cells touching the degree
   * of freedom and then add other cells that take care of possible hanging node
   * constraints.  Using a FE_DGQ finite element, the degrees of freedom are
   * logically considered to be "interior" to the cells so the patch would
   * consist exclusively of the single cell on which the degree of freedom is
   * located.
   *
   * @param[in] dof_handler The DoFHandler which could be built on a
   * Triangulation or a parallel::distributed::Triangulation with a finite
   * element that has degrees of freedom that are logically associated to a
   * vertex, line, quad, or hex.
   * @return A map from the global_dof_index of
   * degrees of freedom on locally relevant cells to vectors containing
   * DoFHandler::active_cell_iterators of cells in the support of the basis
   * function at that degree of freedom.
   */
  template <int dim, int spacedim>
  std::map<
    types::global_dof_index,
    std::vector<typename DoFHandler<dim, spacedim>::active_cell_iterator>>
  get_dof_to_support_patch_map(DoFHandler<dim, spacedim> &dof_handler);


  /*@}*/

  /**
   * @name Dealing with periodic domains
   */
  /*@{*/

  /**
   * Data type that provides all information necessary to create periodicity
   * constraints and a periodic p4est forest with respect to two 'periodic'
   * cell faces.
   */
  template <typename CellIterator>
  struct PeriodicFacePair
  {
    /**
     * The cells associated with the two 'periodic' faces.
     */
    CellIterator cell[2];

    /**
     * The local face indices (with respect to the specified cells) of the two
     * 'periodic' faces.
     */
    unsigned int face_idx[2];

    /**
     * The relative orientation of the first face with respect to the second
     * face as described in orthogonal_equality() and
     * DoFTools::make_periodicity_constraints() (and stored as a bitset).
     */
    std::bitset<3> orientation;

    /**
     * A @p dim $\times$ @p dim rotation matrix that describes how vector
     * valued DoFs of the first face should be modified prior to constraining
     * to the DoFs of the second face.
     *
     * The rotation matrix is used in DoFTools::make_periodicity_constraints()
     * by applying the rotation to all vector valued blocks listed in the
     * parameter @p first_vector_components of the finite element space. For
     * more details see DoFTools::make_periodicity_constraints() and the
     * glossary
     * @ref GlossPeriodicConstraints "glossary entry on periodic conditions".
     */
    FullMatrix<double> matrix;
  };


  /**
   * An orthogonal equality test for faces.
   *
   * @p face1 and @p face2 are considered equal, if a one to one matching
   * between its vertices can be achieved via an orthogonal equality relation.
   *
   * Here, two vertices <tt>v_1</tt> and <tt>v_2</tt> are considered equal, if
   * $M\cdot v_1 + offset - v_2$ is parallel to the unit vector in unit
   * direction @p direction. If the parameter @p matrix is a reference to a
   * spacedim x spacedim matrix, $M$ is set to @p matrix, otherwise $M$ is the
   * identity matrix.
   *
   * If the matching was successful, the _relative_ orientation of @p face1
   * with respect to @p face2 is returned in the bitset @p orientation, where
   * @code
   * orientation[0] -> face_orientation
   * orientation[1] -> face_flip
   * orientation[2] -> face_rotation
   * @endcode
   *
   * In 2D <tt>face_orientation</tt> is always <tt>true</tt>,
   * <tt>face_rotation</tt> is always <tt>false</tt>, and face_flip has the
   * meaning of <tt>line_flip</tt>. More precisely in 3d:
   *
   * <tt>face_orientation</tt>: <tt>true</tt> if @p face1 and @p face2 have
   * the same orientation. Otherwise, the vertex indices of @p face1 match the
   * vertex indices of @p face2 in the following manner:
   *
   * @code
   * face1:           face2:
   *
   * 1 - 3            2 - 3
   * |   |    <-->    |   |
   * 0 - 2            0 - 1
   * @endcode
   *
   * <tt>face_flip</tt>: <tt>true</tt> if the matched vertices are rotated by
   * 180 degrees:
   *
   * @code
   * face1:           face2:
   *
   * 1 - 0            2 - 3
   * |   |    <-->    |   |
   * 3 - 2            0 - 1
   * @endcode
   *
   * <tt>face_rotation</tt>: <tt>true</tt> if the matched vertices are rotated
   * by 90 degrees counterclockwise:
   *
   * @code
   * face1:           face2:
   *
   * 0 - 2            2 - 3
   * |   |    <-->    |   |
   * 1 - 3            0 - 1
   * @endcode
   *
   * and any combination of that... More information on the topic can be found
   * in the
   * @ref GlossFaceOrientation "glossary"
   * article.
   */
  template <typename FaceIterator>
  bool
  orthogonal_equality(
    std::bitset<3> &                                              orientation,
    const FaceIterator &                                          face1,
    const FaceIterator &                                          face2,
    const unsigned int                                            direction,
    const Tensor<1, FaceIterator::AccessorType::space_dimension> &offset =
      Tensor<1, FaceIterator::AccessorType::space_dimension>(),
    const FullMatrix<double> &matrix = FullMatrix<double>());


  /**
   * Same function as above, but doesn't return the actual orientation
   */
  template <typename FaceIterator>
  bool
  orthogonal_equality(
    const FaceIterator &                                          face1,
    const FaceIterator &                                          face2,
    const unsigned int                                            direction,
    const Tensor<1, FaceIterator::AccessorType::space_dimension> &offset =
      Tensor<1, FaceIterator::AccessorType::space_dimension>(),
    const FullMatrix<double> &matrix = FullMatrix<double>());


  /**
   * This function will collect periodic face pairs on the coarsest mesh level
   * of the given @p mesh (a Triangulation or DoFHandler) and add them to the
   * vector @p matched_pairs leaving the original contents intact.
   *
   * Define a 'first' boundary as all boundary faces having boundary_id @p
   * b_id1 and a 'second' boundary consisting of all faces belonging to @p
   * b_id2.
   *
   * This function tries to match all faces belonging to the first boundary
   * with faces belonging to the second boundary with the help of
   * orthogonal_equality().
   *
   * The bitset that is returned inside of PeriodicFacePair encodes the
   * _relative_ orientation of the first face with respect to the second face,
   * see the documentation of orthogonal_equality() for further details.
   *
   * The @p direction refers to the space direction in which periodicity is
   * enforced. When matching periodic faces this vector component is ignored.
   *
   * The @p offset is a vector tangential to the faces that is added to the
   * location of vertices of the 'first' boundary when attempting to match
   * them to the corresponding vertices of the 'second' boundary. This can be
   * used to implement conditions such as $u(0,y)=u(1,y+1)$.
   *
   * Optionally, a $dim\times dim$ rotation @p matrix can be specified that
   * describes how vector valued DoFs of the first face should be modified
   * prior to constraining to the DoFs of the second face. The @p matrix is
   * used in two places. First, @p matrix will be supplied to
   * orthogonal_equality() and used for matching faces: Two vertices $v_1$ and
   * $v_2$ match if $\text{matrix}\cdot v_1 + \text{offset} - v_2$ is parallel
   * to the unit vector in unit direction @p direction. (For more details see
   * DoFTools::make_periodicity_constraints(), the glossary
   * @ref GlossPeriodicConstraints "glossary entry on periodic conditions"
   * and step-45). Second, @p matrix will be stored in the PeriodicFacePair
   * collection @p matched_pairs for further use.
   *
   * @tparam MeshType A type that satisfies the requirements of the
   * @ref ConceptMeshType "MeshType concept".
   *
   * @note The created std::vector can be used in
   * DoFTools::make_periodicity_constraints() and in
   * parallel::distributed::Triangulation::add_periodicity() to enforce
   * periodicity algebraically.
   *
   * @note Because elements will be added to @p matched_pairs (and existing
   * entries will be preserved), it is possible to call this function several
   * times with different boundary ids to generate a vector with all periodic
   * pairs.
   *
   * @note Since the periodic face pairs are found on the coarsest mesh level,
   * it is necessary to ensure that the coarsest level faces have the correct
   * boundary indicators set. In general, this means that one must first set
   * all boundary indicators on the coarse grid before performing any global
   * or local grid refinement.
   */
  template <typename MeshType>
  void
  collect_periodic_faces(
    const MeshType &         mesh,
    const types::boundary_id b_id1,
    const types::boundary_id b_id2,
    const unsigned int       direction,
    std::vector<PeriodicFacePair<typename MeshType::cell_iterator>>
      &                                         matched_pairs,
    const Tensor<1, MeshType::space_dimension> &offset =
      dealii::Tensor<1, MeshType::space_dimension>(),
    const FullMatrix<double> &matrix = FullMatrix<double>());


  /**
   * This compatibility version of collect_periodic_faces() only works on
   * grids with cells in
   * @ref GlossFaceOrientation "standard orientation".
   *
   * Instead of defining a 'first' and 'second' boundary with the help of two
   * boundary_ids this function defines a 'left' boundary as all faces with
   * local face index <code>2*direction</code> and boundary indicator @p b_id
   * and, similarly, a 'right' boundary consisting of all face with local face
   * index <code>2*direction+1</code> and boundary indicator @p b_id. Faces
   * with coordinates only differing in the @p direction component are
   * identified.
   *
   * This function will collect periodic face pairs on the coarsest mesh level
   * and add them to @p matched_pairs leaving the original contents intact.
   *
   * See above function for further details.
   *
   * @note This version of collect_periodic_faces() will not work on
   * meshes with cells not in
   * @ref GlossFaceOrientation "standard orientation".
   */
  template <typename MeshType>
  void
  collect_periodic_faces(
    const MeshType &         mesh,
    const types::boundary_id b_id,
    const unsigned int       direction,
    std::vector<PeriodicFacePair<typename MeshType::cell_iterator>>
      &                                                 matched_pairs,
    const dealii::Tensor<1, MeshType::space_dimension> &offset =
      dealii::Tensor<1, MeshType::space_dimension>(),
    const FullMatrix<double> &matrix = FullMatrix<double>());

  /*@}*/
  /**
   * @name Dealing with boundary and manifold ids
   */
  /*@{*/

  /**
   * Copy boundary ids to manifold ids on faces and edges at the boundary. The
   * default manifold_id for new Triangulation objects is
   * numbers::flat_manifold_id. This function copies the boundary_ids of
   * the boundary faces and edges to the manifold_ids of the same faces and
   * edges, allowing the user to change the boundary_ids and use them for
   * boundary conditions regardless of the geometry, which will use
   * manifold_ids to create new points. Only active cells will be iterated
   * over. This is a function you'd typically call when there is only one
   * active level on your Triangulation. Mesh refinement will then inherit
   * these indicators to child cells, faces, and edges.
   *
   * The optional parameter @p reset_boundary_ids, indicates whether this
   * function should reset the boundary_ids of boundary faces and edges to its
   * default value 0 after copying its value to the manifold_id. By default,
   * boundary_ids are left untouched.
   *
   * @ingroup manifold
   * @relatesalso boundary
   */
  template <int dim, int spacedim>
  void
  copy_boundary_to_manifold_id(Triangulation<dim, spacedim> &tria,
                               const bool reset_boundary_ids = false);

  /**
   * Map the given boundary ids to the given manifold ids on faces and
   * edges at the boundary.
   *
   * This function copies the boundary ids of the boundary faces and
   * edges that are present in the parameter @p src_boundary_ids to
   * the corresponding manifold id in @p dst_manifold_ids, of the same
   * faces and edges.
   *
   * If the optional parameter @p reset_boundary_ids is non empty,
   * each boundary id in @p src_boundary_ids, is replaced with the
   * corresponding boundary id in @p reset_boundary_ids.
   *
   * An exception is thrown if the size of the input vectors do not
   * match. If a boundary id indicated in @p src_boundary_ids is not
   * present in the triangulation, it is simply ignored during the
   * process.
   *
   * @ingroup manifold
   * @relatesalso boundary
   */
  template <int dim, int spacedim>
  void
  map_boundary_to_manifold_ids(
    const std::vector<types::boundary_id> &src_boundary_ids,
    const std::vector<types::manifold_id> &dst_manifold_ids,
    Triangulation<dim, spacedim> &         tria,
    const std::vector<types::boundary_id> &reset_boundary_ids = {});

  /**
   * Copy material ids to manifold ids. The default manifold_id for new
   * Triangulation objects is numbers::flat_manifold_id. When refinements
   * occurs, the Triangulation asks where to locate new points to the
   * underlying manifold.
   *
   * When reading a Triangulation from a supported input format, typical
   * information that can be stored in a file are boundary conditions for
   * boundary faces (which we store in the boundary_id of the faces), material
   * types for cells (which we store in the material_id of the cells) and in
   * some cases subdomain ids for cells (which we store in the subdomain_id of
   * the cell).
   *
   * If you read one of these grids into a Triangulation, you might still want
   * to use the material_id specified in the input file as a manifold_id
   * description. In this case you can associate a Manifold object to internal
   * cells, and this object will be used by the Triangulation to query
   * Manifold objects for new points. This function iterates over active cells
   * and copies the material_ids to the manifold_ids.
   *
   * The optional parameter @p compute_face_ids, indicates whether this
   * function should also set the manifold_ids of the faces (both for internal
   * faces and for faces on the boundary). If set to true, then each face will
   * get a manifold_id equal to the minimum of the surrounding manifold_ids,
   * ensuring that a unique manifold id is selected for each face of the
   * Triangulation. By default, face manifold_ids are not computed.
   *
   * @ingroup manifold
   */
  template <int dim, int spacedim>
  void
  copy_material_to_manifold_id(Triangulation<dim, spacedim> &tria,
                               const bool compute_face_ids = false);

  /**
   * Propagate manifold indicators associated with the cells of the
   * Triangulation @p tria to their co-dimension one and two objects.
   *
   * This function sets the @p manifold_id of faces and edges (both on the
   * interior and on the boundary) to the value returned by the
   * @p disambiguation_function method, called with the set of
   * manifold indicators of the cells that share the same face or edge.
   *
   * By default, the @p disambiguation_function returns
   * numbers::flat_manifold_id when the set has size greater than one (i.e.,
   * when it is not possible to decide what manifold indicator a face or edge
   * should have according to the manifold indicators of the adjacent cells)
   * and it returns the manifold indicator contained in the set when it has
   * dimension one (i.e., when all adjacent cells and faces have the same
   * manifold indicator).
   *
   * The parameter @p overwrite_only_flat_manifold_ids allows you to specify
   * what to do when a face or an edge already has a manifold indicator
   * different from numbers::flat_manifold_id. If the flag is @p true, the edge
   * or face will maintain its original manifold indicator.
   * If it is @p false, then also the manifold indicator of these faces and edges
   * is set according to the return value of the @p disambiguation_function.
   */
  template <int dim, int spacedim>
  void
  assign_co_dimensional_manifold_indicators(
    Triangulation<dim, spacedim> &            tria,
    const std::function<types::manifold_id(
      const std::set<types::manifold_id> &)> &disambiguation_function =
      [](const std::set<types::manifold_id> &manifold_ids) {
        if (manifold_ids.size() == 1)
          return *manifold_ids.begin();
        else
          return numbers::flat_manifold_id;
      },
    bool overwrite_only_flat_manifold_ids = true);
  /*@}*/

  /**
   * Exchange arbitrary data of type @p DataType provided by the function
   * objects from locally owned cells to ghost cells on other processors.
   *
   * After this call, you typically will have received data from @p unpack on
   * every ghost cell as it was given by @p pack on the owning processor.
   * Whether you do or do not receive information to @p unpack on a given
   * ghost cell depends on whether the @p pack function decided that
   * something needs to be sent. It does so using the std_cxx17::optional
   * mechanism: if the std_cxx17::optional return object of the @p pack
   * function is empty, then this implies that no data has to be sent for
   * the locally owned cell it was called on. In that case, @p unpack will
   * also not be called on the ghost cell that corresponds to it on the
   * receiving side. On the other hand, if the std_cxx17::optional object is
   * not empty, then the data stored within it will be sent to the received
   * and the @p unpack function called with it.
   *
   * @tparam DataType The type of the data to be communicated. It is assumed
   *   to be serializable by boost::serialization. In many cases, this
   *   data type can not be deduced by the compiler, e.g., if you provide
   *   lambda functions for the second and third argument
   *   to this function. In this case, you have to explicitly specify
   *   the @p DataType as a template argument to the function call.
   * @tparam MeshType The type of @p mesh.
   *
   * @param mesh A variable of a type that satisfies the requirements of the
   * @ref ConceptMeshType "MeshType concept".
   * @param pack The function that will be called on each locally owned cell
   *   that is a ghost cell somewhere else. As mentioned above, the function
   *   may return a regular data object of type @p DataType to indicate
   *   that data should be sent, or an empty
   *   <code>std_cxx17::optional@<DataType@></code> to indicate that nothing has
   *   to be sent for this cell.
   * @param unpack The function that will be called for each ghost cell
   *   for which data was sent, i.e., for which the @p pack function
   *   on the sending side returned a non-empty std_cxx17::optional object.
   *   The @p unpack function is then called with the data sent by the
   *   processor that owns that cell.
   * @param cell_filter Only cells are communicated where this filter function returns
   *   the value `true`. In the default case, the function returns true on all
   * cells and thus, all relevant cells are communicated.
   *
   * <h4> An example </h4>
   *
   * Here is an example that shows how this function is to be used
   * in a concrete context. It is taken from the code that makes
   * sure that the @p active_fe_index (a single unsigned integer) is
   * transported from locally owned cells where one can set it in
   * DoFHandler objects with hp-capabilities, to the corresponding ghost cells
   * on other processors to ensure that one can query the right value also on
   * those processors:
   * @code
   * using active_cell_iterator =
   *   typename dealii::DoFHandler<dim,spacedim>::active_cell_iterator;
   * auto pack = [] (const active_cell_iterator &cell) -> unsigned int
   *             {
   *               return cell->active_fe_index();
   *             };
   *
   * auto unpack = [] (const active_cell_iterator &cell,
   *                   const unsigned int active_fe_index) -> void
   *               {
   *                 cell->set_active_fe_index(active_fe_index);
   *               };
   *
   * GridTools::exchange_cell_data_to_ghosts<
   *   unsigned int, dealii::DoFHandler<dim,spacedim>> (dof_handler,
   *                                                    pack,
   *                                                    unpack);
   * @endcode
   *
   * You will notice that the @p pack lambda function returns an `unsigned int`,
   * not a `std_cxx17::optional<unsigned int>`. The former converts
   * automatically to the latter, implying that data will always be transported
   * to the other processor.
   *
   * (In reality, the @p unpack function needs to be a bit more
   * complicated because it is not allowed to call
   * DoFAccessor::set_active_fe_index() on ghost cells. Rather, the
   * @p unpack function directly accesses internal data structures. But
   * you get the idea -- the code could, just as well, have exchanged
   * material ids, user indices, boundary indicators, or any kind of other
   * data with similar calls as the ones above.)
   */
  template <typename DataType, typename MeshType>
  void
  exchange_cell_data_to_ghosts(
    const MeshType &                                     mesh,
    const std::function<std_cxx17::optional<DataType>(
      const typename MeshType::active_cell_iterator &)> &pack,
    const std::function<void(const typename MeshType::active_cell_iterator &,
                             const DataType &)> &        unpack,
    const std::function<bool(const typename MeshType::active_cell_iterator &)>
      &cell_filter =
        always_return<typename MeshType::active_cell_iterator, bool>{true});

  /**
   * Exchange arbitrary data of type @p DataType provided by the function
   * objects from locally owned level cells to ghost level cells on other
   * processes.
   *
   * In addition to the parameters of exchange_cell_data_to_ghosts(), this
   * function allows to provide a @p cell_filter function, which can be used to only
   * communicate marked cells. In the default case, all relevant cells are
   * communicated.
   */
  template <typename DataType, typename MeshType>
  void
  exchange_cell_data_to_level_ghosts(
    const MeshType &                                    mesh,
    const std::function<std_cxx17::optional<DataType>(
      const typename MeshType::level_cell_iterator &)> &pack,
    const std::function<void(const typename MeshType::level_cell_iterator &,
                             const DataType &)> &       unpack,
    const std::function<bool(const typename MeshType::level_cell_iterator &)> &
      cell_filter = always_return<typename MeshType::level_cell_iterator, bool>{
        true});

  /* Exchange with all processors of the MPI communicator @p mpi_communicator the vector of bounding
   * boxes @p local_bboxes.
   *
   * This function is meant to exchange bounding boxes describing the locally
   * owned cells in a distributed triangulation obtained with the function
   * GridTools::compute_mesh_predicate_bounding_box .
   *
   * The output vector's size is the number of processes of the MPI
   * communicator:
   * its i-th entry contains the vector @p local_bboxes of the i-th process.
   */
  template <int spacedim>
  std::vector<std::vector<BoundingBox<spacedim>>>
  exchange_local_bounding_boxes(
    const std::vector<BoundingBox<spacedim>> &local_bboxes,
    const MPI_Comm &                          mpi_communicator);

  /**
   * In this collective operation each process provides a vector
   * of bounding boxes and a communicator.
   * All these vectors are gathered on each of the processes,
   * organized in a search tree which, and then returned.
   *
   * The idea is that the vector of bounding boxes describes a
   * relevant property of the computations on each process
   * individually, which could also be of use to other processes. An
   * example would be if the input vector of bounding boxes
   * corresponded to a covering of the locally owned partition of a
   * mesh (see
   * @ref GlossLocallyOwnedCell)
   * of a
   * parallel::distributed::Triangulation object. While these may
   * overlap the bounding boxes of other processes, finding which
   * process owns the cell that encloses a given point is vastly
   * easier if the process trying to figure this out has a list of
   * bounding boxes for each of the other processes at hand.
   *
   * The returned search tree object is an r-tree with packing
   * algorithm, which is provided by boost library. See
   * https://www.boost.org/doc/libs/1_67_0/libs/geometry/doc/html/geometry/spatial_indexes/introduction.html
   * for more information.
   *
   * In the returned tree, each node contains a pair of elements:
   * the first being a bounding box,
   * the second being the rank of the process whose local description
   * contains the bounding box.
   *
   * @note This function is a collective operation.
   */
  template <int spacedim>
  RTree<std::pair<BoundingBox<spacedim>, unsigned int>>
  build_global_description_tree(
    const std::vector<BoundingBox<spacedim>> &local_description,
    const MPI_Comm &                          mpi_communicator);

  /**
   * Collect for a given triangulation all locally relevant vertices that
   * coincide due to periodicity.
   *
   * Coinciding vertices are put into a group, e.g.: [1, 25, 51], which is
   * labeled by an arbitrary element from it, e.g.: "1". All coinciding vertices
   * store the label to its group, so that they can quickly access all the
   * coinciding vertices in that group: e.g.: 51 ->  "1" -> [1, 25, 51]
   *
   * @param[in] tria Triangulation.
   * @param[out] coinciding_vertex_groups A map of equivalence classes (of
   *             coinciding vertices) labeled by an arbitrary element from them.
   *             Vertices not coinciding are ignored.
   * @param[out] vertex_to_coinciding_vertex_group Map of a vertex to the label
   *             of a group of coinciding vertices. Vertices not contained in
   *             this vector are not coinciding with any other vertex.
   */
  template <int dim, int spacedim>
  void
  collect_coinciding_vertices(
    const Triangulation<dim, spacedim> &               tria,
    std::map<unsigned int, std::vector<unsigned int>> &coinciding_vertex_groups,
    std::map<unsigned int, unsigned int> &vertex_to_coinciding_vertex_group);

  /**
   * Return a map that, for each vertex, lists all the processes whose
   * subdomains are adjacent to that vertex.
   *
   * @param[in] tria Triangulation.
   */
  template <int dim, int spacedim>
  std::map<unsigned int, std::set<dealii::types::subdomain_id>>
  compute_vertices_with_ghost_neighbors(
    const Triangulation<dim, spacedim> &tria);

  /**
   * A structure that allows the transfer of cell data of type @p T from one processor
   * to another. It corresponds to a packed buffer that stores a vector of
   * CellId and a vector of type @p T.
   *
   * This class facilitates the transfer by providing the save/load functions
   * that are able to pack up the vector of CellId's and the associated
   * data of type @p T into a stream.
   *
   * Type @p T is assumed to be serializable by <code>boost::serialization</code> (for
   * example <code>unsigned int</code> or <code>std::vector@<double@></code>).
   *
   * @deprecated The implementation in deal.II has been rewritten, making this class
   * obsolete for use within deal.II. Use your own data structures instead.
   */
  template <int dim, typename T>
  struct DEAL_II_DEPRECATED CellDataTransferBuffer
  {
    /**
     * A vector to store IDs of cells to be transferred.
     */
    std::vector<CellId> cell_ids;

    /**
     * A vector of cell data to be transferred.
     */
    std::vector<T> data;

    /**
     * Write the data of this object to a stream for the purpose of
     * serialization using the [BOOST serialization
     * library](https://www.boost.org/doc/libs/1_74_0/libs/serialization/doc/index.html)
     *
     * @pre The user is responsible to keep the size of @p data
     * equal to the size as @p cell_ids .
     */
    template <class Archive>
    void
    save(Archive &ar, const unsigned int) const
    {
      // Implement the code inline as some compilers do otherwise complain
      // about the use of a deprecated interface.
      Assert(cell_ids.size() == data.size(),
             ExcDimensionMismatch(cell_ids.size(), data.size()));
      // archive the cellids in an efficient binary format
      const std::size_t n_cells = cell_ids.size();
      ar &              n_cells;
      for (const auto &id : cell_ids)
        {
          CellId::binary_type binary_cell_id = id.template to_binary<dim>();
          ar &                binary_cell_id;
        }

      ar &data;
    }

    /**
     * Read the data of this object from a stream for the purpose of
     * serialization using the [BOOST serialization
     * library](https://www.boost.org/doc/libs/1_74_0/libs/serialization/doc/index.html).
     * Throw away the previous content.
     */
    template <class Archive>
    void
    load(Archive &ar, const unsigned int)
    {
      // Implement the code inline as some compilers do otherwise complain
      // about the use of a deprecated interface.
      std::size_t n_cells;
      ar &        n_cells;
      cell_ids.clear();
      cell_ids.reserve(n_cells);
      for (unsigned int c = 0; c < n_cells; ++c)
        {
          CellId::binary_type value;
          ar &                value;
          cell_ids.emplace_back(value);
        }
      ar &data;
    }


#ifdef DOXYGEN
    /**
     * Read or write the data of this object to or from a stream for the
     * purpose of serialization using the [BOOST serialization
     * library](https://www.boost.org/doc/libs/1_74_0/libs/serialization/doc/index.html).
     */
    template <class Archive>
    void
    serialize(Archive &archive, const unsigned int version);
#else
    // This macro defines the serialize() method that is compatible with
    // the templated save() and load() method that have been implemented.
    BOOST_SERIALIZATION_SPLIT_MEMBER()
#endif
  };



  /**
   * An implementation of the marching-square (2D) and marching-cube algorithm
   * for creating data structures (vectors of Point and CellData) to
   * create a linear/bilinear surface mesh on the iso line/contour of a
   * scalar field.
   *
   * To improve the approximation of the iso line/contour and the resulting
   * linear surface mesh, one increases the number of subdivision so that the
   * algorithm is not run on a cell but on subcells with vertex values having
   * been interpolated from the cell values.
   *
   * @note The resulting mesh will contain lines in 2D and triangles in 3D.
   *
   * @note The resulting mesh will not be of high quality, since it might
   *   contain cells with very small diameters if the mesh is cut close to a
   *   vertex.
   */
  template <int dim, typename VectorType>
  class MarchingCubeAlgorithm
  {
  public:
    /**
     * Value type of vector.
     */
    using value_type = typename VectorType::value_type;

    /**
     * Constructor.
     */
    MarchingCubeAlgorithm(const Mapping<dim, dim> &      mapping,
                          const FiniteElement<dim, dim> &fe,
                          const unsigned int             n_subdivisions = 1,
                          const double                   tolerance = 1e-10);

    /**
     * Process all locally-owned cells and fill @p vertices and @p cells for all
     * cells that are cut.
     */
    void
    process(const DoFHandler<dim> &         background_dof_handler,
            const VectorType &              ls_vector,
            const double                    iso_level,
            std::vector<Point<dim>> &       vertices,
            std::vector<CellData<dim - 1>> &cells) const;

    /**
     * Process the provided cell and fill @p vertices and @p cells for all cells
     * that are cut.
     *
     * @note The resulting vectors are empty if the cell is not cut.
     */
    void
    process_cell(const typename DoFHandler<dim>::active_cell_iterator &cell,
                 const VectorType &              ls_vector,
                 const double                    iso_level,
                 std::vector<Point<dim>> &       vertices,
                 std::vector<CellData<dim - 1>> &cells) const;

  private:
    /**
     * Internal function to create a quadrature rule with n_subdivisions+1
     * equally-positioned quadrature points.
     */
    static Quadrature<dim>
    create_quadrature_rule(const unsigned int n_subdivisions);

    /**
     * Process a cell.
     */
    void
    process_cell(std::vector<value_type> &       ls_values,
                 const std::vector<Point<dim>> & points,
                 const double                    iso_level,
                 std::vector<Point<dim>> &       vertices,
                 std::vector<CellData<dim - 1>> &cells) const;

    /**
     * Process a sub-cell (2D).
     *
     * @note Subcells with saddle points are ignored. Please increase the number
     *   of subdivisions in this case.
     */
    void
    process_sub_cell(const std::vector<value_type> & ls_values,
                     const std::vector<Point<2>> &   points,
                     const std::vector<unsigned int> mask,
                     const double                    iso_level,
                     std::vector<Point<2>> &         vertices,
                     std::vector<CellData<1>> &      cells) const;

    /**
     * Process a sub-cell (3D).
     */
    void
    process_sub_cell(const std::vector<value_type> & ls_values,
                     const std::vector<Point<3>> &   points,
                     const std::vector<unsigned int> mask,
                     const double                    iso_level,
                     std::vector<Point<3>> &         vertices,
                     std::vector<CellData<2>> &      cells) const;

    /**
     * Number of subdivisions each cell is subdivided into in each direction to
     * improve the approximation.
     */
    const unsigned int n_subdivisions;

    /**
     * Absolute tolerance specifying the minimum distance between a vertex and
     * the cut point so that a line is considered cut.
     */
    const double tolerance;

    /**
     * FEValues used internally and set up with a quadrature rule with the
     * correct number of subdivisions.
     */
    mutable FEValues<dim> fe_values;
  };



  /**
   * @name Exceptions
   */
  /*@{*/

  /**
   * Exception
   */
  DeclException1(ExcInvalidNumberOfPartitions,
                 int,
                 << "The number of partitions you gave is " << arg1
                 << ", but must be greater than zero.");
  /**
   * Exception
   */
  DeclException1(ExcNonExistentSubdomain,
                 int,
                 << "The subdomain id " << arg1
                 << " has no cells associated with it.");
  /**
   * Exception
   */
  DeclException0(ExcTriangulationHasBeenRefined);

  /**
   * Exception
   */
  DeclException1(ExcScalingFactorNotPositive,
                 double,
                 << "The scaling factor must be positive, but it is " << arg1
                 << '.');

  /**
   * Exception
   */
  DeclException1(ExcVertexNotUsed,
                 unsigned int,
                 << "The given vertex with index " << arg1
                 << " is not used in the given triangulation.");

  /*@}*/

} /*namespace GridTools*/


/**
 * An exception that is thrown whenever the edges of a mesh are not
 * orientable.
 *
 * @note for backwards compatibility with the old GridReordering class this
 * exception is not in the GridTools namespace.
 *
 * @ingroup Exceptions
 */
DeclExceptionMsg(ExcMeshNotOrientable,
                 "The edges of the mesh are not consistently orientable.");



/* ----------------- Template function --------------- */

#ifndef DOXYGEN

namespace GridTools
{
  template <int dim>
  double
  cell_measure(
    const std::vector<Point<dim>> &all_vertices,
    const unsigned int (&indices)[GeometryInfo<dim>::vertices_per_cell])
  {
    // We forward call to the ArrayView version:
    const ArrayView<const unsigned int> view(
      indices, GeometryInfo<dim>::vertices_per_cell);
    return cell_measure(all_vertices, view);
  }



  // This specialization is defined here so that the general template in the
  // source file doesn't need to have further 1D overloads for the internal
  // functions it calls.
  template <>
  inline Triangulation<1, 1>::DistortedCellList
  fix_up_distorted_child_cells(const Triangulation<1, 1>::DistortedCellList &,
                               Triangulation<1, 1> &)
  {
    return {};
  }



  template <int dim, typename Predicate, int spacedim>
  void
  transform(const Predicate &             predicate,
            Triangulation<dim, spacedim> &triangulation)
  {
    std::vector<bool> treated_vertices(triangulation.n_vertices(), false);

    // loop over all active cells, and
    // transform those vertices that
    // have not yet been touched. note
    // that we get to all vertices in
    // the triangulation by only
    // visiting the active cells.
    typename Triangulation<dim, spacedim>::active_cell_iterator
      cell = triangulation.begin_active(),
      endc = triangulation.end();
    for (; cell != endc; ++cell)
      for (const unsigned int v : cell->vertex_indices())
        if (treated_vertices[cell->vertex_index(v)] == false)
          {
            // transform this vertex
            cell->vertex(v) = predicate(cell->vertex(v));
            // and mark it as treated
            treated_vertices[cell->vertex_index(v)] = true;
          };


    // now fix any vertices on hanging nodes so that we don't create any holes
    if (dim == 2)
      {
        typename Triangulation<dim, spacedim>::active_cell_iterator
          cell = triangulation.begin_active(),
          endc = triangulation.end();
        for (; cell != endc; ++cell)
          for (const unsigned int face : cell->face_indices())
            if (cell->face(face)->has_children() &&
                !cell->face(face)->at_boundary())
              {
                Assert(cell->reference_cell() ==
                         ReferenceCells::get_hypercube<dim>(),
                       ExcNotImplemented());

                // this line has children
                cell->face(face)->child(0)->vertex(1) =
                  (cell->face(face)->vertex(0) + cell->face(face)->vertex(1)) /
                  2;
              }
      }
    else if (dim == 3)
      {
        typename Triangulation<dim, spacedim>::active_cell_iterator
          cell = triangulation.begin_active(),
          endc = triangulation.end();
        for (; cell != endc; ++cell)
          for (const unsigned int face : cell->face_indices())
            if (cell->face(face)->has_children() &&
                !cell->face(face)->at_boundary())
              {
                Assert(cell->reference_cell() ==
                         ReferenceCells::get_hypercube<dim>(),
                       ExcNotImplemented());

                // this face has hanging nodes
                cell->face(face)->child(0)->vertex(1) =
                  (cell->face(face)->vertex(0) + cell->face(face)->vertex(1)) /
                  2.0;
                cell->face(face)->child(0)->vertex(2) =
                  (cell->face(face)->vertex(0) + cell->face(face)->vertex(2)) /
                  2.0;
                cell->face(face)->child(1)->vertex(3) =
                  (cell->face(face)->vertex(1) + cell->face(face)->vertex(3)) /
                  2.0;
                cell->face(face)->child(2)->vertex(3) =
                  (cell->face(face)->vertex(2) + cell->face(face)->vertex(3)) /
                  2.0;

                // center of the face
                cell->face(face)->child(0)->vertex(3) =
                  (cell->face(face)->vertex(0) + cell->face(face)->vertex(1) +
                   cell->face(face)->vertex(2) + cell->face(face)->vertex(3)) /
                  4.0;
              }
      }

    // Make sure FEValues notices that the mesh has changed
    triangulation.signals.mesh_movement();
  }



  template <class MeshType>
  std::vector<typename MeshType::active_cell_iterator>
  get_active_child_cells(const typename MeshType::cell_iterator &cell)
  {
    std::vector<typename MeshType::active_cell_iterator> child_cells;

    if (cell->has_children())
      {
        for (unsigned int child = 0; child < cell->n_children(); ++child)
          if (cell->child(child)->has_children())
            {
              const std::vector<typename MeshType::active_cell_iterator>
                children = get_active_child_cells<MeshType>(cell->child(child));
              child_cells.insert(child_cells.end(),
                                 children.begin(),
                                 children.end());
            }
          else
            child_cells.push_back(cell->child(child));
      }

    return child_cells;
  }



  template <class MeshType>
  void
  get_active_neighbors(
    const typename MeshType::active_cell_iterator &       cell,
    std::vector<typename MeshType::active_cell_iterator> &active_neighbors)
  {
    active_neighbors.clear();
    for (const unsigned int n : cell->face_indices())
      if (!cell->at_boundary(n))
        {
          if (MeshType::dimension == 1)
            {
              // check children of neighbor. note
              // that in 1d children of the neighbor
              // may be further refined. In 1d the
              // case is simple since we know what
              // children bound to the present cell
              typename MeshType::cell_iterator neighbor_child =
                cell->neighbor(n);
              if (!neighbor_child->is_active())
                {
                  while (neighbor_child->has_children())
                    neighbor_child = neighbor_child->child(n == 0 ? 1 : 0);

                  Assert(neighbor_child->neighbor(n == 0 ? 1 : 0) == cell,
                         ExcInternalError());
                }
              active_neighbors.push_back(neighbor_child);
            }
          else
            {
              if (cell->face(n)->has_children())
                // this neighbor has children. find
                // out which border to the present
                // cell
                for (unsigned int c = 0;
                     c < cell->face(n)->n_active_descendants();
                     ++c)
                  active_neighbors.push_back(
                    cell->neighbor_child_on_subface(n, c));
              else
                {
                  // the neighbor must be active
                  // himself
                  Assert(cell->neighbor(n)->is_active(), ExcInternalError());
                  active_neighbors.push_back(cell->neighbor(n));
                }
            }
        }
  }



  namespace internal
  {
    namespace ProjectToObject
    {
      /**
       * The method GridTools::project_to_object requires taking derivatives
       * along the surface of a simplex. In general these cannot be
       * approximated with finite differences but special differences of the
       * form
       *
       *     df/dx_i - df/dx_j
       *
       * <em>can</em> be approximated. This <code>struct</code> just stores
       * the two derivatives approximated by the stencil (in the case of the
       * example above <code>i</code> and <code>j</code>).
       */
      struct CrossDerivative
      {
        const unsigned int direction_0;
        const unsigned int direction_1;

        CrossDerivative(const unsigned int d0, const unsigned int d1);
      };

      inline CrossDerivative::CrossDerivative(const unsigned int d0,
                                              const unsigned int d1)
        : direction_0(d0)
        , direction_1(d1)
      {}



      /**
       * Standard second-order approximation to the first derivative with a
       * two-point centered scheme. This is used below in a 1D Newton method.
       */
      template <typename F>
      inline auto
      centered_first_difference(const double center,
                                const double step,
                                const F &f) -> decltype(f(center) - f(center))
      {
        return (f(center + step) - f(center - step)) / (2.0 * step);
      }



      /**
       * Standard second-order approximation to the second derivative with a
       * three-point centered scheme. This is used below in a 1D Newton method.
       */
      template <typename F>
      inline auto
      centered_second_difference(const double center,
                                 const double step,
                                 const F &f) -> decltype(f(center) - f(center))
      {
        return (f(center + step) - 2.0 * f(center) + f(center - step)) /
               (step * step);
      }



      /**
       * Fourth order approximation of the derivative
       *
       *     df/dx_i - df/dx_j
       *
       * where <code>i</code> and <code>j</code> are specified by @p
       * cross_derivative. The derivative approximation is at @p center with a
       * step size of @p step and function @p f.
       */
      template <int structdim, typename F>
      inline auto
      cross_stencil(
        const CrossDerivative cross_derivative,
        const Tensor<1, GeometryInfo<structdim>::vertices_per_cell> &center,
        const double                                                 step,
        const F &f) -> decltype(f(center) - f(center))
      {
        Tensor<1, GeometryInfo<structdim>::vertices_per_cell> simplex_vector;
        simplex_vector[cross_derivative.direction_0] = 0.5 * step;
        simplex_vector[cross_derivative.direction_1] = -0.5 * step;
        return (-4.0 * f(center) - 1.0 * f(center + simplex_vector) -
                1.0 / 3.0 * f(center - simplex_vector) +
                16.0 / 3.0 * f(center + 0.5 * simplex_vector)) /
               step;
      }



      /**
       * The optimization algorithm used in GridTools::project_to_object is
       * essentially a gradient descent method. This function computes entries
       * in the gradient of the objective function; see the description in the
       * comments inside GridTools::project_to_object for more information.
       */
      template <int spacedim, int structdim, typename F>
      inline double
      gradient_entry(
        const unsigned int     row_n,
        const unsigned int     dependent_direction,
        const Point<spacedim> &p0,
        const Tensor<1, GeometryInfo<structdim>::vertices_per_cell> &center,
        const double                                                 step,
        const F &                                                    f)
      {
        Assert(row_n < GeometryInfo<structdim>::vertices_per_cell &&
                 dependent_direction <
                   GeometryInfo<structdim>::vertices_per_cell,
               ExcMessage("This function assumes that the last weight is a "
                          "dependent variable (and hence we cannot take its "
                          "derivative directly)."));
        Assert(row_n != dependent_direction,
               ExcMessage(
                 "We cannot differentiate with respect to the variable "
                 "that is assumed to be dependent."));

        const Point<spacedim>     manifold_point = f(center);
        const Tensor<1, spacedim> stencil_value  = cross_stencil<structdim>(
          {row_n, dependent_direction}, center, step, f);
        double entry = 0.0;
        for (unsigned int dim_n = 0; dim_n < spacedim; ++dim_n)
          entry +=
            -2.0 * (p0[dim_n] - manifold_point[dim_n]) * stencil_value[dim_n];
        return entry;
      }

      /**
       * Project onto a d-linear object. This is more accurate than the
       * general algorithm in project_to_object but only works for geometries
       * described by linear, bilinear, or trilinear mappings.
       */
      template <typename Iterator, int spacedim, int structdim>
      Point<spacedim>
      project_to_d_linear_object(const Iterator &       object,
                                 const Point<spacedim> &trial_point)
      {
        // let's look at this for simplicity for a quad (structdim==2) in a
        // space with spacedim>2 (notate trial_point by y): all points on the
        // surface are given by
        //   x(\xi) = sum_i v_i phi_x(\xi)
        // where v_i are the vertices of the quad, and \xi=(\xi_1,\xi_2) are the
        // reference coordinates of the quad. so what we are trying to do is
        // find a point x on the surface that is closest to the point y. there
        // are different ways to solve this problem, but in the end it's a
        // nonlinear problem and we have to find reference coordinates \xi so
        // that J(\xi) = 1/2 || x(\xi)-y ||^2 is minimal. x(\xi) is a function
        // that is structdim-linear in \xi, so J(\xi) is a polynomial of degree
        // 2*structdim that we'd like to minimize. unless structdim==1, we'll
        // have to use a Newton method to find the answer. This leads to the
        // following formulation of Newton steps:
        //
        // Given \xi_k, find \delta\xi_k so that
        //   H_k \delta\xi_k = - F_k
        // where H_k is an approximation to the second derivatives of J at
        // \xi_k, and F_k is the first derivative of J.  We'll iterate this a
        // number of times until the right hand side is small enough. As a
        // stopping criterion, we terminate if ||\delta\xi||<eps.
        //
        // As for the Hessian, the best choice would be
        //   H_k = J''(\xi_k)
        // but we'll opt for the simpler Gauss-Newton form
        //   H_k = A^T A
        // i.e.
        //   (H_k)_{nm} = \sum_{i,j} v_i*v_j *
        //                   \partial_n phi_i *
        //                   \partial_m phi_j
        // we start at xi=(0.5, 0.5).
        Point<structdim> xi;
        for (unsigned int d = 0; d < structdim; ++d)
          xi[d] = 0.5;

        Point<spacedim> x_k;
        for (const unsigned int i : GeometryInfo<structdim>::vertex_indices())
          x_k += object->vertex(i) *
                 GeometryInfo<structdim>::d_linear_shape_function(xi, i);

        do
          {
            Tensor<1, structdim> F_k;
            for (const unsigned int i :
                 GeometryInfo<structdim>::vertex_indices())
              F_k +=
                (x_k - trial_point) * object->vertex(i) *
                GeometryInfo<structdim>::d_linear_shape_function_gradient(xi,
                                                                          i);

            Tensor<2, structdim> H_k;
            for (const unsigned int i :
                 GeometryInfo<structdim>::vertex_indices())
              for (const unsigned int j :
                   GeometryInfo<structdim>::vertex_indices())
                {
                  Tensor<2, structdim> tmp = outer_product(
                    GeometryInfo<structdim>::d_linear_shape_function_gradient(
                      xi, i),
                    GeometryInfo<structdim>::d_linear_shape_function_gradient(
                      xi, j));
                  H_k += (object->vertex(i) * object->vertex(j)) * tmp;
                }

            const Tensor<1, structdim> delta_xi = -invert(H_k) * F_k;
            xi += delta_xi;

            x_k = Point<spacedim>();
            for (const unsigned int i :
                 GeometryInfo<structdim>::vertex_indices())
              x_k += object->vertex(i) *
                     GeometryInfo<structdim>::d_linear_shape_function(xi, i);

            if (delta_xi.norm() < 1e-7)
              break;
          }
        while (true);

        return x_k;
      }
    } // namespace ProjectToObject
  }   // namespace internal



  namespace internal
  {
    // We hit an internal compiler error in ICC 15 if we define this as a lambda
    // inside the project_to_object function below.
    template <int structdim>
    inline bool
    weights_are_ok(
      const Tensor<1, GeometryInfo<structdim>::vertices_per_cell> &v)
    {
      // clang has trouble figuring out structdim here, so define it
      // again:
      static const std::size_t n_vertices_per_cell =
        Tensor<1, GeometryInfo<structdim>::vertices_per_cell>::
          n_independent_components;
      std::array<double, n_vertices_per_cell> copied_weights;
      for (unsigned int i = 0; i < n_vertices_per_cell; ++i)
        {
          copied_weights[i] = v[i];
          if (v[i] < 0.0 || v[i] > 1.0)
            return false;
        }

      // check the sum: try to avoid some roundoff errors by summing in order
      std::sort(copied_weights.begin(), copied_weights.end());
      const double sum =
        std::accumulate(copied_weights.begin(), copied_weights.end(), 0.0);
      return std::abs(sum - 1.0) < 1e-10; // same tolerance used in manifold.cc
    }
  } // namespace internal

  template <typename Iterator>
  Point<Iterator::AccessorType::space_dimension>
  project_to_object(
    const Iterator &                                      object,
    const Point<Iterator::AccessorType::space_dimension> &trial_point)
  {
    const int spacedim  = Iterator::AccessorType::space_dimension;
    const int structdim = Iterator::AccessorType::structure_dimension;

    Point<spacedim> projected_point = trial_point;

    if (structdim >= spacedim)
      return projected_point;
    else if (structdim == 1 || structdim == 2)
      {
        using namespace internal::ProjectToObject;
        // Try to use the special flat algorithm for quads (this is better
        // than the general algorithm in 3D). This does not take into account
        // whether projected_point is outside the quad, but we optimize along
        // lines below anyway:
        const int                      dim = Iterator::AccessorType::dimension;
        const Manifold<dim, spacedim> &manifold = object->get_manifold();
        if (structdim == 2 && dynamic_cast<const FlatManifold<dim, spacedim> *>(
                                &manifold) != nullptr)
          {
            projected_point =
              project_to_d_linear_object<Iterator, spacedim, structdim>(
                object, trial_point);
          }
        else
          {
            // We want to find a point on the convex hull (defined by the
            // vertices of the object and the manifold description) that is
            // relatively close to the trial point. This has a few issues:
            //
            // 1. For a general convex hull we are not guaranteed that a unique
            //    minimum exists.
            // 2. The independent variables in the optimization process are the
            //    weights given to Manifold::get_new_point, which must sum to 1,
            //    so we cannot use standard finite differences to approximate a
            //    gradient.
            //
            // There is not much we can do about 1., but for 2. we can derive
            // finite difference stencils that work on a structdim-dimensional
            // simplex and rewrite the optimization problem to use those
            // instead. Consider the structdim 2 case and let
            //
            // F(c0, c1, c2, c3) = Manifold::get_new_point(vertices, {c0, c1,
            // c2, c3})
            //
            // where {c0, c1, c2, c3} are the weights for the four vertices on
            // the quadrilateral. We seek to minimize the Euclidean distance
            // between F(...) and trial_point. We can solve for c3 in terms of
            // the other weights and get, for one coordinate direction
            //
            // d/dc0 ((x0 - F(c0, c1, c2, 1 - c0 - c1 - c2))^2)
            //      = -2(x0 - F(...)) (d/dc0 F(...) - d/dc3 F(...))
            //
            // where we substitute back in for c3 after taking the
            // derivative. We can compute a stencil for the cross derivative
            // d/dc0 - d/dc3: this is exactly what cross_stencil approximates
            // (and gradient_entry computes the sum over the independent
            // variables). Below, we somewhat arbitrarily pick the last
            // component as the dependent one.
            //
            // Since we can now calculate derivatives of the objective
            // function we can use gradient descent to minimize it.
            //
            // Of course, this is much simpler in the structdim = 1 case (we
            // could rewrite the projection as a 1D optimization problem), but
            // to reduce the potential for bugs we use the same code in both
            // cases.
            const double step_size = object->diameter() / 64.0;

            constexpr unsigned int n_vertices_per_cell =
              GeometryInfo<structdim>::vertices_per_cell;

            std::array<Point<spacedim>, n_vertices_per_cell> vertices;
            for (unsigned int vertex_n = 0; vertex_n < n_vertices_per_cell;
                 ++vertex_n)
              vertices[vertex_n] = object->vertex(vertex_n);

            auto get_point_from_weights =
              [&](const Tensor<1, n_vertices_per_cell> &weights)
              -> Point<spacedim> {
              return object->get_manifold().get_new_point(
                make_array_view(vertices.begin(), vertices.end()),
                make_array_view(weights.begin_raw(), weights.end_raw()));
            };

            // pick the initial weights as (normalized) inverse distances from
            // the trial point:
            Tensor<1, n_vertices_per_cell> guess_weights;
            double                         guess_weights_sum = 0.0;
            for (unsigned int vertex_n = 0; vertex_n < n_vertices_per_cell;
                 ++vertex_n)
              {
                const double distance =
                  vertices[vertex_n].distance(trial_point);
                if (distance == 0.0)
                  {
                    guess_weights           = 0.0;
                    guess_weights[vertex_n] = 1.0;
                    guess_weights_sum       = 1.0;
                    break;
                  }
                else
                  {
                    guess_weights[vertex_n] = 1.0 / distance;
                    guess_weights_sum += guess_weights[vertex_n];
                  }
              }
            guess_weights /= guess_weights_sum;
            Assert(internal::weights_are_ok<structdim>(guess_weights),
                   ExcInternalError());

            // The optimization algorithm consists of two parts:
            //
            // 1. An outer loop where we apply the gradient descent algorithm.
            // 2. An inner loop where we do a line search to find the optimal
            //    length of the step one should take in the gradient direction.
            //
            for (unsigned int outer_n = 0; outer_n < 40; ++outer_n)
              {
                const unsigned int dependent_direction =
                  n_vertices_per_cell - 1;
                Tensor<1, n_vertices_per_cell> current_gradient;
                for (unsigned int row_n = 0; row_n < n_vertices_per_cell;
                     ++row_n)
                  {
                    if (row_n != dependent_direction)
                      {
                        current_gradient[row_n] =
                          gradient_entry<spacedim, structdim>(
                            row_n,
                            dependent_direction,
                            trial_point,
                            guess_weights,
                            step_size,
                            get_point_from_weights);

                        current_gradient[dependent_direction] -=
                          current_gradient[row_n];
                      }
                  }

                // We need to travel in the -gradient direction, as noted
                // above, but we may not want to take a full step in that
                // direction; instead, guess that we will go -0.5*gradient and
                // do quasi-Newton iteration to pick the best multiplier. The
                // goal is to find a scalar alpha such that
                //
                // F(x - alpha g)
                //
                // is minimized, where g is the gradient and F is the
                // objective function. To find the optimal value we find roots
                // of the derivative of the objective function with respect to
                // alpha by Newton iteration, where we approximate the first
                // and second derivatives of F(x - alpha g) with centered
                // finite differences.
                double gradient_weight = -0.5;
                auto   gradient_weight_objective_function =
                  [&](const double gradient_weight_guess) -> double {
                  return (trial_point -
                          get_point_from_weights(guess_weights +
                                                 gradient_weight_guess *
                                                   current_gradient))
                    .norm_square();
                };

                for (unsigned int inner_n = 0; inner_n < 10; ++inner_n)
                  {
                    const double update_numerator = centered_first_difference(
                      gradient_weight,
                      step_size,
                      gradient_weight_objective_function);
                    const double update_denominator =
                      centered_second_difference(
                        gradient_weight,
                        step_size,
                        gradient_weight_objective_function);

                    // avoid division by zero. Note that we limit the gradient
                    // weight below
                    if (std::abs(update_denominator) == 0.0)
                      break;
                    gradient_weight =
                      gradient_weight - update_numerator / update_denominator;

                    // Put a fairly lenient bound on the largest possible
                    // gradient (things tend to be locally flat, so the gradient
                    // itself is usually small)
                    if (std::abs(gradient_weight) > 10)
                      {
                        gradient_weight = -10.0;
                        break;
                      }
                  }

                // It only makes sense to take convex combinations with weights
                // between zero and one. If the update takes us outside of this
                // region then rescale the update to stay within the region and
                // try again
                Tensor<1, n_vertices_per_cell> tentative_weights =
                  guess_weights + gradient_weight * current_gradient;

                double new_gradient_weight = gradient_weight;
                for (unsigned int iteration_count = 0; iteration_count < 40;
                     ++iteration_count)
                  {
                    if (internal::weights_are_ok<structdim>(tentative_weights))
                      break;

                    for (unsigned int i = 0; i < n_vertices_per_cell; ++i)
                      {
                        if (tentative_weights[i] < 0.0)
                          {
                            tentative_weights -=
                              (tentative_weights[i] / current_gradient[i]) *
                              current_gradient;
                          }
                        if (tentative_weights[i] < 0.0 ||
                            1.0 < tentative_weights[i])
                          {
                            new_gradient_weight /= 2.0;
                            tentative_weights =
                              guess_weights +
                              new_gradient_weight * current_gradient;
                          }
                      }
                  }

                // the update might still send us outside the valid region, so
                // check again and quit if the update is still not valid
                if (!internal::weights_are_ok<structdim>(tentative_weights))
                  break;

                // if we cannot get closer by traveling in the gradient
                // direction then quit
                if (get_point_from_weights(tentative_weights)
                      .distance(trial_point) <
                    get_point_from_weights(guess_weights).distance(trial_point))
                  guess_weights = tentative_weights;
                else
                  break;
                Assert(internal::weights_are_ok<structdim>(guess_weights),
                       ExcInternalError());
              }
            Assert(internal::weights_are_ok<structdim>(guess_weights),
                   ExcInternalError());
            projected_point = get_point_from_weights(guess_weights);
          }

        // if structdim == 2 and the optimal point is not on the interior then
        // we may be able to get a more accurate result by projecting onto the
        // lines.
        if (structdim == 2)
          {
            std::array<Point<spacedim>, GeometryInfo<structdim>::lines_per_cell>
              line_projections;
            for (unsigned int line_n = 0;
                 line_n < GeometryInfo<structdim>::lines_per_cell;
                 ++line_n)
              {
                line_projections[line_n] =
                  project_to_object(object->line(line_n), trial_point);
              }
            std::sort(line_projections.begin(),
                      line_projections.end(),
                      [&](const Point<spacedim> &a, const Point<spacedim> &b) {
                        return a.distance(trial_point) <
                               b.distance(trial_point);
                      });
            if (line_projections[0].distance(trial_point) <
                projected_point.distance(trial_point))
              projected_point = line_projections[0];
          }
      }
    else
      {
        Assert(false, ExcNotImplemented());
        return projected_point;
      }

    return projected_point;
  }



  namespace internal
  {
    template <typename DataType,
              typename MeshType,
              typename MeshCellIteratorType>
    inline void
    exchange_cell_data(
      const MeshType &mesh,
      const std::function<
        std_cxx17::optional<DataType>(const MeshCellIteratorType &)> &pack,
      const std::function<void(const MeshCellIteratorType &, const DataType &)>
        &                                                         unpack,
      const std::function<bool(const MeshCellIteratorType &)> &   cell_filter,
      const std::function<void(
        const std::function<void(const MeshCellIteratorType &,
                                 const types::subdomain_id)> &)> &process_cells,
      const std::function<std::set<types::subdomain_id>(
        const parallel::TriangulationBase<MeshType::dimension,
                                          MeshType::space_dimension> &)>
        &compute_ghost_owners)
    {
#  ifndef DEAL_II_WITH_MPI
      (void)mesh;
      (void)pack;
      (void)unpack;
      (void)cell_filter;
      (void)process_cells;
      (void)compute_ghost_owners;
      Assert(false, ExcNeedsMPI());
#  else
      constexpr int dim      = MeshType::dimension;
      constexpr int spacedim = MeshType::space_dimension;
      auto          tria =
        dynamic_cast<const parallel::TriangulationBase<dim, spacedim> *>(
          &mesh.get_triangulation());
      Assert(
        tria != nullptr,
        ExcMessage(
          "The function exchange_cell_data_to_ghosts() only works with parallel triangulations."));

      if (const auto tria = dynamic_cast<
            const parallel::shared::Triangulation<dim, spacedim> *>(
            &mesh.get_triangulation()))
        {
          Assert(
            tria->with_artificial_cells(),
            ExcMessage(
              "The functions GridTools::exchange_cell_data_to_ghosts() and "
              "GridTools::exchange_cell_data_to_level_ghosts() can only "
              "operate on a single layer of ghost cells. However, you have "
              "given a Triangulation object of type "
              "parallel::shared::Triangulation without artificial cells "
              "resulting in arbitrary numbers of ghost layers."));
        }

      // build list of cells to request for each neighbor
      std::set<dealii::types::subdomain_id> ghost_owners =
        compute_ghost_owners(*tria);
      std::map<dealii::types::subdomain_id,
               std::vector<typename CellId::binary_type>>
        neighbor_cell_list;

      for (const auto ghost_owner : ghost_owners)
        neighbor_cell_list[ghost_owner] = {};

      process_cells([&](const auto &cell, const auto key) -> void {
        if (cell_filter(cell))
          {
            constexpr int spacedim = MeshType::space_dimension;
            neighbor_cell_list[key].emplace_back(
              cell->id().template to_binary<spacedim>());
          }
      });

      Assert(ghost_owners.size() == neighbor_cell_list.size(),
             ExcInternalError());


      // Before sending & receiving, make sure we protect this section with
      // a mutex:
      static Utilities::MPI::CollectiveMutex      mutex;
      Utilities::MPI::CollectiveMutex::ScopedLock lock(
        mutex, tria->get_communicator());

      const int mpi_tag =
        Utilities::MPI::internal::Tags::exchange_cell_data_request;
      const int mpi_tag_reply =
        Utilities::MPI::internal::Tags::exchange_cell_data_reply;

      // send our requests
      std::vector<MPI_Request> requests(ghost_owners.size());
      {
        unsigned int idx = 0;
        for (const auto &it : neighbor_cell_list)
          {
            // send the data about the relevant cells
            const int ierr = MPI_Isend(it.second.data(),
                                       it.second.size() * sizeof(it.second[0]),
                                       MPI_BYTE,
                                       it.first,
                                       mpi_tag,
                                       tria->get_communicator(),
                                       &requests[idx]);
            AssertThrowMPI(ierr);
            ++idx;
          }
      }

      // receive requests and reply with the results
      std::vector<MPI_Request>       reply_requests(ghost_owners.size());
      std::vector<std::vector<char>> sendbuffers(ghost_owners.size());

      for (unsigned int idx = 0; idx < ghost_owners.size(); ++idx)
        {
          MPI_Status status;
          int        ierr = MPI_Probe(MPI_ANY_SOURCE,
                               mpi_tag,
                               tria->get_communicator(),
                               &status);
          AssertThrowMPI(ierr);

          int len;
          ierr = MPI_Get_count(&status, MPI_BYTE, &len);
          AssertThrowMPI(ierr);
          Assert(len % sizeof(typename CellId::binary_type) == 0,
                 ExcInternalError());

          const unsigned int n_cells =
            len / sizeof(typename CellId::binary_type);
          std::vector<typename CellId::binary_type> cells_with_requests(
            n_cells);
          std::vector<DataType> data_to_send;
          data_to_send.reserve(n_cells);
          std::vector<bool> cell_carries_data(n_cells, false);

          ierr = MPI_Recv(cells_with_requests.data(),
                          len,
                          MPI_BYTE,
                          status.MPI_SOURCE,
                          status.MPI_TAG,
                          tria->get_communicator(),
                          &status);
          AssertThrowMPI(ierr);

          // store data for each cell
          for (unsigned int c = 0; c < static_cast<unsigned int>(n_cells); ++c)
            {
              const auto cell =
                tria->create_cell_iterator(CellId(cells_with_requests[c]));

              MeshCellIteratorType mesh_it(tria,
                                           cell->level(),
                                           cell->index(),
                                           &mesh);

              const std_cxx17::optional<DataType> data = pack(mesh_it);
              if (data)
                {
                  data_to_send.emplace_back(*data);
                  cell_carries_data[c] = true;
                }
            }

          // collect data for sending the reply in a buffer

          // (a) make room for storing the local offsets in case we receive
          // other data
          sendbuffers[idx].resize(sizeof(std::size_t));

          // (b) append the actual data and store how much memory it
          // corresponds to, which we then insert into the leading position of
          // the sendbuffer
          std::size_t size_of_send =
            Utilities::pack(data_to_send,
                            sendbuffers[idx],
                            /*enable_compression*/ false);
          std::memcpy(sendbuffers[idx].data(),
                      &size_of_send,
                      sizeof(std::size_t));

          // (c) append information of certain cells that got left out in case
          // we need it
          if (data_to_send.size() < n_cells)
            Utilities::pack(cell_carries_data,
                            sendbuffers[idx],
                            /*enable_compression*/ false);

          // send data
          ierr = MPI_Isend(sendbuffers[idx].data(),
                           sendbuffers[idx].size(),
                           MPI_BYTE,
                           status.MPI_SOURCE,
                           mpi_tag_reply,
                           tria->get_communicator(),
                           &reply_requests[idx]);
          AssertThrowMPI(ierr);
        }

      // finally receive the replies
      std::vector<char> receive;
      for (unsigned int id = 0; id < neighbor_cell_list.size(); ++id)
        {
          MPI_Status status;
          int        ierr = MPI_Probe(MPI_ANY_SOURCE,
                               mpi_tag_reply,
                               tria->get_communicator(),
                               &status);
          AssertThrowMPI(ierr);

          int len;
          ierr = MPI_Get_count(&status, MPI_BYTE, &len);
          AssertThrowMPI(ierr);

          receive.resize(len);

          ierr = MPI_Recv(receive.data(),
                          len,
                          MPI_BYTE,
                          status.MPI_SOURCE,
                          status.MPI_TAG,
                          tria->get_communicator(),
                          &status);
          AssertThrowMPI(ierr);

          // (a) first determine the length of the data section in the
          // received buffer
          auto        data_iterator = receive.begin();
          std::size_t size_of_received_data =
            Utilities::unpack<std::size_t>(data_iterator,
                                           data_iterator + sizeof(std::size_t));
          data_iterator += sizeof(std::size_t);

          // (b) unpack the data section in the indicated region
          auto received_data = Utilities::unpack<std::vector<DataType>>(
            data_iterator,
            data_iterator + size_of_received_data,
            /*enable_compression*/ false);
          data_iterator += size_of_received_data;

          // (c) check if the received data contained fewer entries than the
          // number of cells we identified in the beginning, in which case we
          // need to extract the boolean vector with the relevant information
          const std::vector<typename CellId::binary_type> &this_cell_list =
            neighbor_cell_list[status.MPI_SOURCE];
          AssertIndexRange(received_data.size(), this_cell_list.size() + 1);
          std::vector<bool> cells_with_data;
          if (received_data.size() < this_cell_list.size())
            {
              cells_with_data = Utilities::unpack<std::vector<bool>>(
                data_iterator, receive.end(), /*enable_compression*/ false);
              AssertDimension(cells_with_data.size(), this_cell_list.size());
            }

          // (d) go through the received data and call the user-provided
          // unpack function
          auto received_data_iterator = received_data.begin();
          for (unsigned int c = 0; c < this_cell_list.size(); ++c)
            if (cells_with_data.empty() || cells_with_data[c])
              {
                const typename Triangulation<dim, spacedim>::cell_iterator
                  tria_cell = tria->create_cell_iterator(this_cell_list[c]);

                MeshCellIteratorType cell(tria,
                                          tria_cell->level(),
                                          tria_cell->index(),
                                          &mesh);

                unpack(cell, *received_data_iterator);
                ++received_data_iterator;
              }
        }

      // make sure that all communication is finished
      // when we leave this function.
      if (requests.size() > 0)
        {
          const int ierr =
            MPI_Waitall(requests.size(), requests.data(), MPI_STATUSES_IGNORE);
          AssertThrowMPI(ierr);
        }
      if (reply_requests.size() > 0)
        {
          const int ierr = MPI_Waitall(reply_requests.size(),
                                       reply_requests.data(),
                                       MPI_STATUSES_IGNORE);
          AssertThrowMPI(ierr);
        }


#  endif // DEAL_II_WITH_MPI
    }

  } // namespace internal

  template <typename DataType, typename MeshType>
  inline void
  exchange_cell_data_to_ghosts(
    const MeshType &                                     mesh,
    const std::function<std_cxx17::optional<DataType>(
      const typename MeshType::active_cell_iterator &)> &pack,
    const std::function<void(const typename MeshType::active_cell_iterator &,
                             const DataType &)> &        unpack,
    const std::function<bool(const typename MeshType::active_cell_iterator &)>
      &cell_filter)
  {
#  ifndef DEAL_II_WITH_MPI
    (void)mesh;
    (void)pack;
    (void)unpack;
    (void)cell_filter;
    Assert(false, ExcNeedsMPI());
#  else
    internal::exchange_cell_data<DataType,
                                 MeshType,
                                 typename MeshType::active_cell_iterator>(
      mesh,
      pack,
      unpack,
      cell_filter,
      [&](const auto &process) {
        for (const auto &cell : mesh.active_cell_iterators())
          if (cell->is_ghost())
            process(cell, cell->subdomain_id());
      },
      [](const auto &tria) { return tria.ghost_owners(); });
#  endif
  }



  template <typename DataType, typename MeshType>
  inline void
  exchange_cell_data_to_level_ghosts(
    const MeshType &                                    mesh,
    const std::function<std_cxx17::optional<DataType>(
      const typename MeshType::level_cell_iterator &)> &pack,
    const std::function<void(const typename MeshType::level_cell_iterator &,
                             const DataType &)> &       unpack,
    const std::function<bool(const typename MeshType::level_cell_iterator &)>
      &cell_filter)
  {
#  ifndef DEAL_II_WITH_MPI
    (void)mesh;
    (void)pack;
    (void)unpack;
    (void)cell_filter;
    Assert(false, ExcNeedsMPI());
#  else
    internal::exchange_cell_data<DataType,
                                 MeshType,
                                 typename MeshType::level_cell_iterator>(
      mesh,
      pack,
      unpack,
      cell_filter,
      [&](const auto &process) {
        for (const auto &cell : mesh.cell_iterators())
          if (cell->level_subdomain_id() !=
                dealii::numbers::artificial_subdomain_id &&
              !cell->is_locally_owned_on_level())
            process(cell, cell->level_subdomain_id());
      },
      [](const auto &tria) { return tria.level_ghost_owners(); });
#  endif
  }
} // namespace GridTools

#endif // DOXYGEN

DEAL_II_NAMESPACE_CLOSE

#endif
