smoothness_estimator.cc

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//
// Copyright (C) 2018 - 2023 by the deal.II authors
//
// This file is part of the deal.II library.
//
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#include <deal.II/base/quadrature.h>
#include <deal.II/base/signaling_nan.h>
 
#include <deal.II/dofs/dof_handler.h>
 
#include <deal.II/fe/fe_series.h>
 
#include <deal.II/grid/filtered_iterator.h>
 
#include <deal.II/hp/q_collection.h>
 
#include <deal.II/lac/block_vector.h>
#include <deal.II/lac/la_parallel_block_vector.h>
#include <deal.II/lac/la_parallel_vector.h>
#include <deal.II/lac/la_vector.h>
#include <deal.II/lac/petsc_block_vector.h>
#include <deal.II/lac/petsc_vector.h>
#include <deal.II/lac/trilinos_epetra_vector.h>
#include <deal.II/lac/trilinos_parallel_block_vector.h>
#include <deal.II/lac/trilinos_vector.h>
#include <deal.II/lac/vector.h>
 
#include <deal.II/numerics/smoothness_estimator.h>
 
#include <algorithm>
#include <cmath>
#include <limits>
#include <utility>
 
 
DEAL_II_NAMESPACE_OPEN
 
 
namespace SmoothnessEstimator
{
  namespace
  {
    template <int dim, typename CoefficientType>
    void
    resize(Table<dim, CoefficientType> &coeff, const unsigned int N)
    {
      TableIndices<dim> size;
      for (unsigned int d = 0; d < dim; ++d)
        size[d] = N;
      coeff.reinit(size);
    }
  } // namespace
 
 
 
  namespace Legendre
  {
    namespace
    {
      template <int dim>
      std::pair<bool, unsigned int>
      index_sum_less_than_N(const TableIndices<dim> &ind, const unsigned int N)
      {
        unsigned int v = 0;
        for (unsigned int i = 0; i < dim; ++i)
          v += ind[i];
 
        return std::make_pair((v < N), v);
      }
    } // namespace
 
 
 
    template <int dim, int spacedim, typename VectorType>
    void
    coefficient_decay(FESeries::Legendre<dim, spacedim> &fe_legendre,
                      const DoFHandler<dim, spacedim> &  dof_handler,
                      const VectorType &                 solution,
                      Vector<float> &                    smoothness_indicators,
                      const VectorTools::NormType        regression_strategy,
                      const double smallest_abs_coefficient,
                      const bool   only_flagged_cells)
    {
      using number = typename VectorType::value_type;
      using number_coeff =
        typename FESeries::Legendre<dim, spacedim>::CoefficientType;
 
      smoothness_indicators.reinit(
        dof_handler.get_triangulation().n_active_cells());
 
      unsigned int             n_modes;
      Table<dim, number_coeff> expansion_coefficients;
 
      Vector<number>      local_dof_values;
      std::vector<double> converted_indices;
      std::pair<std::vector<unsigned int>, std::vector<double>> res;
      for (const auto &cell : dof_handler.active_cell_iterators() |
                                IteratorFilters::LocallyOwnedCell())
        {
          if (!only_flagged_cells || cell->refine_flag_set() ||
              cell->coarsen_flag_set())
            {
              n_modes = fe_legendre.get_n_coefficients_per_direction(
                cell->active_fe_index());
              resize(expansion_coefficients, n_modes);
 
              local_dof_values.reinit(cell->get_fe().n_dofs_per_cell());
              cell->get_dof_values(solution, local_dof_values);
 
              fe_legendre.calculate(local_dof_values,
                                    cell->active_fe_index(),
                                    expansion_coefficients);
 
              // We fit our exponential decay of expansion coefficients to the
              // provided regression_strategy on each possible value of |k|.
              // To this end, we use FESeries::process_coefficients() to
              // rework coefficients into the desired format.
              res = FESeries::process_coefficients<dim>(
                expansion_coefficients,
                [n_modes](const TableIndices<dim> &indices) {
                  return index_sum_less_than_N(indices, n_modes);
                },
                regression_strategy,
                smallest_abs_coefficient);
 
              Assert(res.first.size() == res.second.size(), ExcInternalError());
 
              // Last, do the linear regression.
              float regularity = std::numeric_limits<float>::infinity();
              if (res.first.size() > 1)
                {
                  // Prepare linear equation for the logarithmic least squares
                  // fit.
                  converted_indices.assign(res.first.begin(), res.first.end());
 
                  for (auto &residual_element : res.second)
                    residual_element = std::log(residual_element);
 
                  const std::pair<double, double> fit =
                    FESeries::linear_regression(converted_indices, res.second);
                  regularity = static_cast<float>(-fit.first);
                }
 
              smoothness_indicators(cell->active_cell_index()) = regularity;
            }
          else
            smoothness_indicators(cell->active_cell_index()) =
              numbers::signaling_nan<float>();
        }
    }
 
 
 
    template <int dim, int spacedim, typename VectorType>
    void
    coefficient_decay_per_direction(
      FESeries::Legendre<dim, spacedim> &fe_legendre,
      const DoFHandler<dim, spacedim> &  dof_handler,
      const VectorType &                 solution,
      Vector<float> &                    smoothness_indicators,
      const ComponentMask &              coefficients_predicate,
      const double                       smallest_abs_coefficient,
      const bool                         only_flagged_cells)
    {
      Assert(smallest_abs_coefficient >= 0.,
             ExcMessage("smallest_abs_coefficient should be non-negative."));
 
      using number = typename VectorType::value_type;
      using number_coeff =
        typename FESeries::Legendre<dim, spacedim>::CoefficientType;
 
      smoothness_indicators.reinit(
        dof_handler.get_triangulation().n_active_cells());
 
      unsigned int             n_modes;
      Table<dim, number_coeff> expansion_coefficients;
      Vector<number>           local_dof_values;
 
      // auxiliary vector to do linear regression
      const unsigned int max_degree =
        dof_handler.get_fe_collection().max_degree();
 
      std::vector<double> x, y;
      x.reserve(max_degree);
      y.reserve(max_degree);
 
      for (const auto &cell : dof_handler.active_cell_iterators() |
                                IteratorFilters::LocallyOwnedCell())
        {
          if (!only_flagged_cells || cell->refine_flag_set() ||
              cell->coarsen_flag_set())
            {
              n_modes = fe_legendre.get_n_coefficients_per_direction(
                cell->active_fe_index());
              resize(expansion_coefficients, n_modes);
 
              const unsigned int pe = cell->get_fe().degree;
              Assert(pe > 0, ExcInternalError());
 
              // since we use coefficients with indices [1,pe] in each
              // direction, the number of coefficients we need to calculate is
              // at least N=pe+1
              AssertIndexRange(pe, n_modes);
 
              local_dof_values.reinit(cell->get_fe().n_dofs_per_cell());
              cell->get_dof_values(solution, local_dof_values);
 
              fe_legendre.calculate(local_dof_values,
                                    cell->active_fe_index(),
                                    expansion_coefficients);
 
              // choose the smallest decay of coefficients in each direction,
              // i.e. the maximum decay slope k_v as in exp(-k_v)
              double k_v = std::numeric_limits<double>::infinity();
              for (unsigned int d = 0; d < dim; ++d)
                {
                  x.resize(0);
                  y.resize(0);
 
                  // will use all non-zero coefficients allowed by the
                  // predicate function
                  for (unsigned int i = 0; i <= pe; ++i)
                    if (coefficients_predicate[i])
                      {
                        TableIndices<dim> ind;
                        ind[d] = i;
                        const double coeff_abs =
                          std::abs(expansion_coefficients(ind));
 
                        if (coeff_abs > smallest_abs_coefficient)
                          {
                            x.push_back(i);
                            y.push_back(std::log(coeff_abs));
                          }
                      }
 
                  // in case we don't have enough non-zero coefficient to fit,
                  // skip this direction
                  if (x.size() < 2)
                    continue;
 
                  const std::pair<double, double> fit =
                    FESeries::linear_regression(x, y);
 
                  // decay corresponds to negative slope
                  // take the lesser negative slope along each direction
                  k_v = std::min(k_v, -fit.first);
                }
 
              smoothness_indicators(cell->active_cell_index()) =
                static_cast<float>(k_v);
            }
          else
            smoothness_indicators(cell->active_cell_index()) =
              numbers::signaling_nan<float>();
        }
    }
 
 
 
    template <int dim, int spacedim>
    FESeries::Legendre<dim, spacedim>
    default_fe_series(const hp::FECollection<dim, spacedim> &fe_collection,
                      const unsigned int                     component)
    {
      // Default number of coefficients per direction.
      //
      // With a number of modes equal to the polynomial degree plus two for each
      // finite element, the smoothness estimation algorithm tends to produce
      // stable results.
      std::vector<unsigned int> n_coefficients_per_direction;
      for (unsigned int i = 0; i < fe_collection.size(); ++i)
        n_coefficients_per_direction.push_back(fe_collection[i].degree + 2);
 
      // Default quadrature collection.
      //
      // We initialize a FESeries::Legendre expansion object object which will
      // be used to calculate the expansion coefficients. In addition to the
      // hp::FECollection, we need to provide quadrature rules hp::QCollection
      // for integration on the reference cell.
      // We will need to assemble the expansion matrices for each of the finite
      // elements we deal with, i.e. the matrices F_k,j. We have to do that for
      // each of the finite elements in use. To that end we need a quadrature
      // rule. As a default, we use the same quadrature formula for each finite
      // element, namely a Gauss formula that yields exact results for the
      // highest order Legendre polynomial used.
      //
      // We start with the zeroth Legendre polynomial which is just a constant,
      // so the highest Legendre polynomial will be of order (n_modes - 1).
      hp::QCollection<dim> q_collection;
      for (unsigned int i = 0; i < fe_collection.size(); ++i)
        {
          const QGauss<dim>  quadrature(n_coefficients_per_direction[i]);
          const QSorted<dim> quadrature_sorted(quadrature);
          q_collection.push_back(quadrature_sorted);
        }
 
      return FESeries::Legendre<dim, spacedim>(n_coefficients_per_direction,
                                               fe_collection,
                                               q_collection,
                                               component);
    }
  } // namespace Legendre
 
 
 
  namespace Fourier
  {
    namespace
    {
      template <int dim>
      std::pair<bool, unsigned int>
      index_norm_greater_than_zero_and_less_than_N_squared(
        const TableIndices<dim> &ind,
        const unsigned int       N)
      {
        unsigned int v = 0;
        for (unsigned int i = 0; i < dim; ++i)
          v += ind[i] * ind[i];
 
        return std::make_pair((v > 0 && v < N * N), v);
      }
    } // namespace
 
 
 
    template <int dim, int spacedim, typename VectorType>
    void
    coefficient_decay(FESeries::Fourier<dim, spacedim> &fe_fourier,
                      const DoFHandler<dim, spacedim> & dof_handler,
                      const VectorType &                solution,
                      Vector<float> &                   smoothness_indicators,
                      const VectorTools::NormType       regression_strategy,
                      const double smallest_abs_coefficient,
                      const bool   only_flagged_cells)
    {
      using number = typename VectorType::value_type;
      using number_coeff =
        typename FESeries::Fourier<dim, spacedim>::CoefficientType;
 
      smoothness_indicators.reinit(
        dof_handler.get_triangulation().n_active_cells());
 
      unsigned int             n_modes;
      Table<dim, number_coeff> expansion_coefficients;
 
      Vector<number>      local_dof_values;
      std::vector<double> ln_k;
      std::pair<std::vector<unsigned int>, std::vector<double>> res;
      for (const auto &cell : dof_handler.active_cell_iterators() |
                                IteratorFilters::LocallyOwnedCell())
        {
          if (!only_flagged_cells || cell->refine_flag_set() ||
              cell->coarsen_flag_set())
            {
              n_modes = fe_fourier.get_n_coefficients_per_direction(
                cell->active_fe_index());
              resize(expansion_coefficients, n_modes);
 
              // Inside the loop, we first need to get the values of the local
              // degrees of freedom and then need to compute the series
              // expansion by multiplying this vector with the matrix @f${\cal
              // F}@f$ corresponding to this finite element.
              local_dof_values.reinit(cell->get_fe().n_dofs_per_cell());
              cell->get_dof_values(solution, local_dof_values);
 
              fe_fourier.calculate(local_dof_values,
                                   cell->active_fe_index(),
                                   expansion_coefficients);
 
              // We fit our exponential decay of expansion coefficients to the
              // provided regression_strategy on each possible value of |k|.
              // To this end, we use FESeries::process_coefficients() to
              // rework coefficients into the desired format.
              res = FESeries::process_coefficients<dim>(
                expansion_coefficients,
                [n_modes](const TableIndices<dim> &indices) {
                  return index_norm_greater_than_zero_and_less_than_N_squared(
                    indices, n_modes);
                },
                regression_strategy,
                smallest_abs_coefficient);
 
              Assert(res.first.size() == res.second.size(), ExcInternalError());
 
              // Last, do the linear regression.
              float regularity = std::numeric_limits<float>::infinity();
              if (res.first.size() > 1)
                {
                  // Prepare linear equation for the logarithmic least squares
                  // fit.
                  //
                  // First, calculate ln(|k|).
                  //
                  // For Fourier expansion, this translates to
                  // ln(2*pi*sqrt(predicate)) = ln(2*pi) + 0.5*ln(predicate).
                  // Since we are just interested in the slope of a linear
                  // regression later, we omit the ln(2*pi) factor.
                  ln_k.resize(res.first.size());
                  for (unsigned int f = 0; f < res.first.size(); ++f)
                    ln_k[f] = 0.5 * std::log(static_cast<double>(res.first[f]));
 
                  // Second, calculate ln(U_k).
                  for (auto &residual_element : res.second)
                    residual_element = std::log(residual_element);
 
                  const std::pair<double, double> fit =
                    FESeries::linear_regression(ln_k, res.second);
                  // Compute regularity s = mu - dim/2
                  regularity = static_cast<float>(-fit.first) -
                               ((dim > 1) ? (.5 * dim) : 0);
                }
 
              // Store result in the vector of estimated values for each cell.
              smoothness_indicators(cell->active_cell_index()) = regularity;
            }
          else
            smoothness_indicators(cell->active_cell_index()) =
              numbers::signaling_nan<float>();
        }
    }
 
 
 
    template <int dim, int spacedim, typename VectorType>
    void
    coefficient_decay_per_direction(
      FESeries::Fourier<dim, spacedim> &fe_fourier,
      const DoFHandler<dim, spacedim> & dof_handler,
      const VectorType &                solution,
      Vector<float> &                   smoothness_indicators,
      const ComponentMask &             coefficients_predicate,
      const double                      smallest_abs_coefficient,
      const bool                        only_flagged_cells)
    {
      Assert(smallest_abs_coefficient >= 0.,
             ExcMessage("smallest_abs_coefficient should be non-negative."));
 
      using number = typename VectorType::value_type;
      using number_coeff =
        typename FESeries::Fourier<dim, spacedim>::CoefficientType;
 
      smoothness_indicators.reinit(
        dof_handler.get_triangulation().n_active_cells());
 
      unsigned int             n_modes;
      Table<dim, number_coeff> expansion_coefficients;
      Vector<number>           local_dof_values;
 
      // auxiliary vector to do linear regression
      const unsigned int max_degree =
        dof_handler.get_fe_collection().max_degree();
 
      std::vector<double> x, y;
      x.reserve(max_degree);
      y.reserve(max_degree);
 
      for (const auto &cell : dof_handler.active_cell_iterators() |
                                IteratorFilters::LocallyOwnedCell())
        {
          if (!only_flagged_cells || cell->refine_flag_set() ||
              cell->coarsen_flag_set())
            {
              n_modes = fe_fourier.get_n_coefficients_per_direction(
                cell->active_fe_index());
              resize(expansion_coefficients, n_modes);
 
              const unsigned int pe = cell->get_fe().degree;
              Assert(pe > 0, ExcInternalError());
 
              // since we use coefficients with indices [1,pe] in each
              // direction, the number of coefficients we need to calculate is
              // at least N=pe+1
              AssertIndexRange(pe, n_modes);
 
              local_dof_values.reinit(cell->get_fe().n_dofs_per_cell());
              cell->get_dof_values(solution, local_dof_values);
 
              fe_fourier.calculate(local_dof_values,
                                   cell->active_fe_index(),
                                   expansion_coefficients);
 
              // choose the smallest decay of coefficients in each direction,
              // i.e. the maximum decay slope k_v as in exp(-k_v)
              double k_v = std::numeric_limits<double>::infinity();
              for (unsigned int d = 0; d < dim; ++d)
                {
                  x.resize(0);
                  y.resize(0);
 
                  // will use all non-zero coefficients allowed by the
                  // predicate function
                  //
                  // skip i=0 because of logarithm
                  for (unsigned int i = 1; i <= pe; ++i)
                    if (coefficients_predicate[i])
                      {
                        TableIndices<dim> ind;
                        ind[d] = i;
                        const double coeff_abs =
                          std::abs(expansion_coefficients(ind));
 
                        if (coeff_abs > smallest_abs_coefficient)
                          {
                            x.push_back(std::log(i));
                            y.push_back(std::log(coeff_abs));
                          }
                      }
 
                  // in case we don't have enough non-zero coefficient to fit,
                  // skip this direction
                  if (x.size() < 2)
                    continue;
 
                  const std::pair<double, double> fit =
                    FESeries::linear_regression(x, y);
 
                  // decay corresponds to negative slope
                  // take the lesser negative slope along each direction
                  k_v = std::min(k_v, -fit.first);
                }
 
              smoothness_indicators(cell->active_cell_index()) =
                static_cast<float>(k_v);
            }
          else
            smoothness_indicators(cell->active_cell_index()) =
              numbers::signaling_nan<float>();
        }
    }
 
 
 
    template <int dim, int spacedim>
    FESeries::Fourier<dim, spacedim>
    default_fe_series(const hp::FECollection<dim, spacedim> &fe_collection,
                      const unsigned int                     component)
    {
      // Default number of coefficients per direction.
      //
      // Since we omit the zero-th mode in the Fourier decay strategy, make sure
      // that we have at least two modes to work with per finite element. With a
      // number of modes equal to the polynomial degree plus two for each finite
      // element, the smoothness estimation algorithm tends to produce stable
      // results.
      std::vector<unsigned int> n_coefficients_per_direction;
      for (unsigned int i = 0; i < fe_collection.size(); ++i)
        n_coefficients_per_direction.push_back(fe_collection[i].degree + 2);
 
      // Default quadrature collection.
      //
      // We initialize a series expansion object object which will be used to
      // calculate the expansion coefficients. In addition to the
      // hp::FECollection, we need to provide quadrature rules hp::QCollection
      // for integration on the reference cell.
      // We will need to assemble the expansion matrices for each of the finite
      // elements we deal with, i.e. the matrices F_k,j. We have to do that for
      // each of the finite elements in use. To that end we need a quadrature
      // rule. As a default, we use the same quadrature formula for each finite
      // element, namely one that is obtained by iterating a 5-point Gauss
      // formula as many times as the maximal exponent we use for the term
      // exp(ikx). Since the first mode corresponds to k = 0, the maximal wave
      // number is k = n_modes - 1.
      const QGauss<1>      base_quadrature(5);
      hp::QCollection<dim> q_collection;
      for (unsigned int i = 0; i < fe_collection.size(); ++i)
        {
          const QIterated<dim> quadrature(base_quadrature,
                                          n_coefficients_per_direction[i] - 1);
          const QSorted<dim>   quadrature_sorted(quadrature);
          q_collection.push_back(quadrature_sorted);
        }
 
      return FESeries::Fourier<dim, spacedim>(n_coefficients_per_direction,
                                              fe_collection,
                                              q_collection,
                                              component);
    }
  } // namespace Fourier
} // namespace SmoothnessEstimator
 
 
// explicit instantiations
#include "smoothness_estimator.inst"
 
DEAL_II_NAMESPACE_CLOSE