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GMLS_Host.cpp
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1 // @HEADER
2 // *****************************************************************************
3 // Compadre: COMpatible PArticle Discretization and REmap Toolkit
4 //
5 // Copyright 2018 NTESS and the Compadre contributors.
6 // SPDX-License-Identifier: BSD-2-Clause
7 // *****************************************************************************
8 // @HEADER
9 #include <iostream>
10 #include <string>
11 #include <vector>
12 #include <map>
13 #include <stdlib.h>
14 #include <cstdio>
15 #include <random>
16 
17 #include <Compadre_Config.h>
18 #include <Compadre_GMLS.hpp>
19 #include <Compadre_Evaluator.hpp>
20 #include "GMLS_Tutorial.hpp"
21 #include "CommandLineProcessor.hpp"
22 
23 #ifdef COMPADRE_USE_MPI
24 #include <mpi.h>
25 #endif
26 
27 #include <Kokkos_Timer.hpp>
28 #include <Kokkos_Core.hpp>
29 
30 using namespace Compadre;
31 
32 int main (int argc, char* args[])
33 {
34 
35 #ifdef COMPADRE_USE_MPI
36  MPI_Init(&argc, &args);
37 #endif
38 
39 bool all_passed = true;
40 
41 {
42 
43  CommandLineProcessor clp(argc, args);
44  auto order = clp.order;
45  auto dimension = clp.dimension;
46  auto number_target_coords = clp.number_target_coords;
47  auto constraint_name = clp.constraint_name;
48  auto solver_name = clp.solver_name;
49  auto problem_name = clp.problem_name;
50 
51  const double failure_tolerance = 1e-9;
52 
53  const int offset = 15;
54  std::mt19937 rng(50);
55  const int min_neighbors = 1*Compadre::GMLS::getNP(order);
56  const int max_neighbors = 1*Compadre::GMLS::getNP(order)*1.15;
57  std::cout << min_neighbors << " " << max_neighbors << std::endl;
58  std::uniform_int_distribution<int> gen_num_neighbors(min_neighbors, max_neighbors); // uniform, unbiased
59 
60 
61  Kokkos::initialize(argc, args);
62  Kokkos::Timer timer;
63  Kokkos::Profiling::pushRegion("Setup");
64 
65 
66  const int N = 40000;
67  std::uniform_int_distribution<int> gen_neighbor_number(offset, N); // 0 to 10 are junk (part of test)
68 
69 
70  Kokkos::View<int**, Kokkos::HostSpace> neighbor_lists("neighbor lists", number_target_coords, max_neighbors+1); // first column is # of neighbors
71  Kokkos::View<double**, Kokkos::HostSpace> source_coords("neighbor coordinates", N, dimension);
72  Kokkos::View<double*, Kokkos::HostSpace> epsilon("h supports", number_target_coords);
73 
74  for (int i=0; i<number_target_coords; i++) {
75  epsilon(i) = 0.5;
76  }
77 
78 // // fake coordinates not to be used
79  for(int i = 0; i < offset; i++){
80  for(int j = 0; j < dimension; j++){
81  source_coords(i,j) = 0.1;
82  }
83  }
84 
85  // filling others with random coordinates
86  for(int i = offset; i < N; i++){ //ignore first ten entries
87  double randx = (2.0*(double)rand() / (double) RAND_MAX - 1.0)*epsilon(0)/2.0;
88  double randy = (2.0*(double)rand() / (double) RAND_MAX - 1.0)*epsilon(0)/2.0;
89  double randz = (2.0*(double)rand() / (double) RAND_MAX - 1.0)*epsilon(0)/2.0;
90  source_coords(i,0) = randx;
91  if (dimension>1) source_coords(i,1) = randy;
92  if (dimension>2) source_coords(i,2) = randz;
93  }
94 
95  const double target_epsilon = 0.1;
96  // fill target coords
97  Kokkos::View<double**, Kokkos::HostSpace> target_coords ("target coordinates", number_target_coords, dimension);
98  for(int i = 0; i < number_target_coords; i++){ //ignore first ten entries
99  double randx = (2.0*(double)rand() / (double) RAND_MAX - 1.0)*target_epsilon/2.0;
100  double randy = (2.0*(double)rand() / (double) RAND_MAX - 1.0)*target_epsilon/2.0;
101  double randz = (2.0*(double)rand() / (double) RAND_MAX - 1.0)*target_epsilon/2.0;
102  target_coords(i,0) = randx;
103  if (dimension>1) target_coords(i,1) = randy;
104  if (dimension>2) target_coords(i,2) = randz;
105  }
106 
107  // randomly fill neighbor lists
108  for (int i=0; i<number_target_coords; i++) {
109 // int r = gen_num_neighbors(rng);
110 // assert(r<source_coords.extent(0)-offset);
111  int r = max_neighbors;
112  neighbor_lists(i,0) = r; // number of neighbors is random between max and min
113 
114  for(int j=0; j<r; j++){
115  neighbor_lists(i,j+1) = offset + j + 1;
116 // bool placed = false;
117 // while (!placed) {
118 // int ind = gen_neighbor_number(rng);
119 // bool located = false;
120 // for (int k=1; k<j+1; k++) {
121 // if (neighbor_lists(i,k) == ind) {
122 // located = true;
123 // break;
124 // }
125 // }
126 // if (!located) {
127 // neighbor_lists(i,j+1) = ind;
128 // placed = true;
129 // } // neighbors can be from 10,...,N-1
130 // }
131  }
132  }
133 
134  Kokkos::Profiling::popRegion();
135  timer.reset();
136 
137  GMLS my_GMLS(order, dimension,
138  solver_name.c_str(), problem_name.c_str(), constraint_name.c_str(),
139  2 /*manifold order*/);
140  my_GMLS.setProblemData(neighbor_lists, source_coords, target_coords, epsilon);
141  my_GMLS.setWeightingParameter(10);
142 
143  std::vector<TargetOperation> lro(3);
144  lro[0] = ScalarPointEvaluation;
145  lro[1] = LaplacianOfScalarPointEvaluation;
146  lro[2] = GradientOfScalarPointEvaluation;
147  my_GMLS.addTargets(lro);
148  // add two more targets individually to test addTargets(...) function
149  my_GMLS.addTargets(DivergenceOfVectorPointEvaluation);
150  my_GMLS.addTargets(CurlOfVectorPointEvaluation);
151  my_GMLS.generateAlphas();
152 
153  double instantiation_time = timer.seconds();
154  std::cout << "Took " << instantiation_time << "s to complete instantiation." << std::endl;
155 
156  Kokkos::Profiling::pushRegion("Creating Data");
157 
158 
159  // need Kokkos View storing true solution
160  Kokkos::View<double*, Kokkos::HostSpace> sampling_data("samples of true solution", source_coords.extent(0));
161  Kokkos::View<double**, Kokkos::HostSpace> gradient_sampling_data("samples of true gradient", source_coords.extent(0), dimension);
162  Kokkos::View<double**, Kokkos::LayoutLeft, Kokkos::HostSpace> divergence_sampling_data("samples of true solution for divergence test", source_coords.extent(0), dimension);
163  Kokkos::parallel_for("Sampling Manufactured Solutions", Kokkos::RangePolicy<Kokkos::DefaultHostExecutionSpace>(0,source_coords.extent(0)), KOKKOS_LAMBDA(const int i) {
164  double xval = source_coords(i,0);
165  double yval = (dimension>1) ? source_coords(i,1) : 0;
166  double zval = (dimension>2) ? source_coords(i,2) : 0;
167  sampling_data(i) = trueSolution(xval, yval, zval, order, dimension);
168  double true_grad[3] = {0,0,0};
169  trueGradient(true_grad, xval, yval,zval, order, dimension);
170  for (int j=0; j<dimension; ++j) {
171  divergence_sampling_data(i,j) = divergenceTestSamples(xval, yval, zval, j, dimension);
172  gradient_sampling_data(i,j) = true_grad[j];
173  }
174  });
175  Kokkos::Profiling::popRegion();
176 
177  Evaluator gmls_evaluator(&my_GMLS);
178 
179  for (int i=0; i<number_target_coords; i++) {
180 
181  Kokkos::Profiling::pushRegion("Apply Alphas to Data");
182 
183  double GMLS_value = gmls_evaluator.applyAlphasToDataSingleComponentSingleTargetSite(sampling_data, 0, ScalarPointEvaluation, i, 0, 0, 0, 0, 0);
184  //for (int j = 0; j< neighbor_lists(i,0); j++){
185  // double xval = source_coords(neighbor_lists(i,j+1),0);
186  // double yval = (dimension>1) ? source_coords(neighbor_lists(i,j+1),1) : 0;
187  // double zval = (dimension>2) ? source_coords(neighbor_lists(i,j+1),2) : 0;
188  // GMLS_value += gmls_evaluator.getAlpha0TensorTo0Tensor(ScalarPointEvaluation, i, j)*trueSolution(xval, yval, zval, order, dimension);
189  //}
190 
191  double GMLS_Laplacian = gmls_evaluator.applyAlphasToDataSingleComponentSingleTargetSite(sampling_data, 0, LaplacianOfScalarPointEvaluation, i, 0, 0, 0, 0, 0);
192  //double GMLS_Laplacian = 0.0;
193  //for (int j = 0; j< neighbor_lists(i,0); j++){
194  // double xval = source_coords(neighbor_lists(i,j+1),0);
195  // double yval = (dimension>1) ? source_coords(neighbor_lists(i,j+1),1) : 0;
196  // double zval = (dimension>2) ? source_coords(neighbor_lists(i,j+1),2) : 0;
197  // GMLS_Laplacian += gmls_evaluator.getAlpha0TensorTo0Tensor(LaplacianOfScalarPointEvaluation, i, j)*trueSolution(xval, yval, zval, order, dimension);
198  //}
199 
200  double GMLS_GradX = gmls_evaluator.applyAlphasToDataSingleComponentSingleTargetSite(sampling_data, 0, GradientOfScalarPointEvaluation, i, 0, 0, 0, 0, 0);
201  //double GMLS_GradX = 0.0;
202  //for (int j = 0; j< neighbor_lists(i,0); j++){
203  // double xval = source_coords(neighbor_lists(i,j+1),0);
204  // double yval = (dimension>1) ? source_coords(neighbor_lists(i,j+1),1) : 0;
205  // double zval = (dimension>2) ? source_coords(neighbor_lists(i,j+1),2) : 0;
206  // GMLS_GradX += gmls_evaluator.getAlpha0TensorTo1Tensor(GradientOfScalarPointEvaluation, i, 0, j)*trueSolution(xval, yval, zval, order, dimension);
207  //}
208 
209  double GMLS_GradY = (dimension>1) ? gmls_evaluator.applyAlphasToDataSingleComponentSingleTargetSite(sampling_data, 0, GradientOfScalarPointEvaluation, i, 0, 1, 0, 0, 0) : 0;
210  //double GMLS_GradY = 0.0;
211  //if (dimension>1) {
212  // for (int j = 0; j< neighbor_lists(i,0); j++){
213  // double xval = source_coords(neighbor_lists(i,j+1),0);
214  // double yval = source_coords(neighbor_lists(i,j+1),1);
215  // double zval = (dimension>2) ? source_coords(neighbor_lists(i,j+1),2) : 0;
216  // GMLS_GradY += gmls_evaluator.getAlpha0TensorTo1Tensor(GradientOfScalarPointEvaluation, i, 1, j)*trueSolution(xval, yval, zval, order, dimension);
217  // }
218  //}
219 
220  double GMLS_GradZ = (dimension>2) ? gmls_evaluator.applyAlphasToDataSingleComponentSingleTargetSite(sampling_data, 0, GradientOfScalarPointEvaluation, i, 0, 2, 0, 0, 0) : 0;
221  //double GMLS_GradZ = 0.0;
222  //if (dimension>2) {
223  // for (int j = 0; j< neighbor_lists(i,0); j++){
224  // double xval = source_coords(neighbor_lists(i,j+1),0);
225  // double yval = source_coords(neighbor_lists(i,j+1),1);
226  // double zval = source_coords(neighbor_lists(i,j+1),2);
227  // GMLS_GradZ += gmls_evaluator.getAlpha0TensorTo1Tensor(GradientOfScalarPointEvaluation, i, 2, j)*trueSolution(xval, yval, zval, order, dimension);
228  // }
229  //}
230 
231  double GMLS_Divergence = gmls_evaluator.applyAlphasToDataSingleComponentSingleTargetSite(gradient_sampling_data, 0, DivergenceOfVectorPointEvaluation, i, 0, 0, 0, 0, 0);
232  if (dimension>1) GMLS_Divergence += gmls_evaluator.applyAlphasToDataSingleComponentSingleTargetSite(gradient_sampling_data, 1, DivergenceOfVectorPointEvaluation, i, 0, 0, 0, 1, 0);
233  if (dimension>2) GMLS_Divergence += gmls_evaluator.applyAlphasToDataSingleComponentSingleTargetSite(gradient_sampling_data, 2, DivergenceOfVectorPointEvaluation, i, 0, 0, 0, 2, 0);
234 
235  //double GMLS_Divergence = gmls_evaluator.applyAlphasToDataSingleComponentSingleTargetSite(Kokkos::subview(gradient_sampling_data,Kokkos::ALL,0), 0, DivergenceOfVectorPointEvaluation, i, 0, 0, 0, 0);
236  //if (dimension>1) GMLS_Divergence += gmls_evaluator.applyAlphasToDataSingleComponentSingleTargetSite(Kokkos::subview(gradient_sampling_data,Kokkos::ALL,1), 1, DivergenceOfVectorPointEvaluation, i, 0, 0, 1, 0);
237  //if (dimension>2) GMLS_Divergence += gmls_evaluator.applyAlphasToDataSingleComponentSingleTargetSite(Kokkos::subview(gradient_sampling_data,Kokkos::ALL,2), 2, DivergenceOfVectorPointEvaluation, i, 0, 0, 2, 0);
238  //double GMLS_Divergence = 0.0;
239  //for (int j = 0; j< neighbor_lists(i,0); j++){
240  // double xval = source_coords(neighbor_lists(i,j+1),0);
241  // double yval = (dimension>1) ? source_coords(neighbor_lists(i,j+1),1) : 0;
242  // double zval = (dimension>2) ? source_coords(neighbor_lists(i,j+1),2) : 0;
243  // // TODO: use different functions for the vector components
244  // if (use_arbitrary_order_divergence) {
245  // GMLS_Divergence += gmls_evaluator.getAlpha1TensorTo0Tensor(DivergenceOfVectorPointEvaluation, i, j, 0)*trueSolution(xval, yval, zval, order, dimension);
246  // if (dimension>1) GMLS_Divergence += gmls_evaluator.getAlpha1TensorTo0Tensor(DivergenceOfVectorPointEvaluation, i, j, 1)*trueSolution(xval, yval, zval, order, dimension);
247  // if (dimension>2) GMLS_Divergence += gmls_evaluator.getAlpha1TensorTo0Tensor(DivergenceOfVectorPointEvaluation, i, j, 2)*trueSolution(xval, yval, zval, order, dimension);
248  // } else {
249  // for (int k=0; k<dimension; ++k) {
250  // GMLS_Divergence += gmls_evaluator.getAlpha1TensorTo0Tensor(DivergenceOfVectorPointEvaluation, i, j, k)*divergenceTestSamples(xval, yval, zval, k, dimension);
251  // }
252  // }
253  //}
254 
255  double GMLS_CurlX = 0.0;
256  double GMLS_CurlY = 0.0;
257  double GMLS_CurlZ = 0.0;
258  if (dimension>1) {
259  for (int j=0; j<dimension; ++j) {
260  GMLS_CurlX += gmls_evaluator.applyAlphasToDataSingleComponentSingleTargetSite(divergence_sampling_data, j, CurlOfVectorPointEvaluation, i, 0, 0, 0, j, 0);
261  GMLS_CurlY += gmls_evaluator.applyAlphasToDataSingleComponentSingleTargetSite(divergence_sampling_data, j, CurlOfVectorPointEvaluation, i, 0, 1, 0, j, 0);
262  }
263  }
264 
265  if (dimension>2) {
266  for (int j=0; j<dimension; ++j) {
267  GMLS_CurlZ += gmls_evaluator.applyAlphasToDataSingleComponentSingleTargetSite(divergence_sampling_data, j, CurlOfVectorPointEvaluation, i, 0, 2, 0, j, 0);
268  }
269 
270  }
271 
272  Kokkos::Profiling::popRegion();
273  //if (dimension>1) {
274  // for (int j = 0; j< neighbor_lists(i,0); j++){
275  // double xval = source_coords(neighbor_lists(i,j+1),0);
276  // double yval = source_coords(neighbor_lists(i,j+1),1);
277  // double zval = (dimension>2) ? source_coords(neighbor_lists(i,j+1),2) : 0;
278  // for (int k=0; k<dimension; ++k) {
279  // GMLS_CurlX += my_GMLS.getAlpha1TensorTo1Tensor(CurlOfVectorPointEvaluation, i, 0, j, k)*divergenceTestSamples(xval, yval, zval, k, dimension);
280  // }
281  // }
282 
283  // for (int j = 0; j< neighbor_lists(i,0); j++){
284  // double xval = source_coords(neighbor_lists(i,j+1),0);
285  // double yval = source_coords(neighbor_lists(i,j+1),1);
286  // double zval = (dimension>2) ? source_coords(neighbor_lists(i,j+1),2) : 0;
287  // for (int k=0; k<dimension; ++k) {
288  // GMLS_CurlY += my_GMLS.getAlpha1TensorTo1Tensor(CurlOfVectorPointEvaluation, i, 1, j, k)*divergenceTestSamples(xval, yval, zval, k, dimension);
289  // }
290  // }
291  //}
292 
293  //if (dimension>2) {
294  // for (int j = 0; j< neighbor_lists(i,0); j++){
295  // double xval = source_coords(neighbor_lists(i,j+1),0);
296  // double yval = source_coords(neighbor_lists(i,j+1),1);
297  // double zval = source_coords(neighbor_lists(i,j+1),2);
298  // for (int k=0; k<dimension; ++k) {
299  // GMLS_CurlZ += my_GMLS.getAlpha1TensorTo1Tensor(CurlOfVectorPointEvaluation, i, 2, j, k)*divergenceTestSamples(xval, yval, zval, k, dimension);
300  // }
301  // }
302  //}
303  //
304  Kokkos::Profiling::pushRegion("Comparison");
305 
306  double xval = target_coords(i,0);
307  double yval = (dimension>1) ? target_coords(i,1) : 0;
308  double zval = (dimension>2) ? target_coords(i,2) : 0;
309 
310  double actual_value = trueSolution(xval, yval, zval, order, dimension);
311  double actual_Laplacian = trueLaplacian(xval, yval, zval, order, dimension);
312  double actual_Gradient[3] = {0,0,0};
313  trueGradient(actual_Gradient, xval, yval, zval, order, dimension);
314  double actual_Divergence;
315  actual_Divergence = trueLaplacian(xval, yval, zval, order, dimension);
316 
317  double actual_CurlX = 0;
318  double actual_CurlY = 0;
319  double actual_CurlZ = 0;
320  if (dimension>1) {
321  actual_CurlX = curlTestSolution(xval, yval, zval, 0, dimension);
322  actual_CurlY = curlTestSolution(xval, yval, zval, 1, dimension);
323  }
324  if (dimension>2) {
325  actual_CurlZ = curlTestSolution(xval, yval, zval, 2, dimension);
326  }
327 
328 // fprintf(stdout, "Reconstructed value: %f \n", GMLS_value);
329 // fprintf(stdout, "Actual value: %f \n", actual_value);
330 // fprintf(stdout, "Reconstructed Laplacian: %f \n", GMLS_Laplacian);
331 // fprintf(stdout, "Actual Laplacian: %f \n", actual_Laplacian);
332 
333  if(GMLS_value!=GMLS_value || std::abs(actual_value - GMLS_value) > failure_tolerance) {
334  all_passed = false;
335  std::cout << "Failed Actual by: " << std::abs(actual_value - GMLS_value) << std::endl;
336  }
337 
338  if(std::abs(actual_Laplacian - GMLS_Laplacian) > failure_tolerance) {
339  all_passed = false;
340  std::cout << "Failed Laplacian by: " << std::abs(actual_Laplacian - GMLS_Laplacian) << std::endl;
341  }
342 
343  if(std::abs(actual_Gradient[0] - GMLS_GradX) > failure_tolerance) {
344  all_passed = false;
345  std::cout << "Failed GradX by: " << std::abs(actual_Gradient[0] - GMLS_GradX) << std::endl;
346  }
347 
348  if (dimension>1) {
349  if(std::abs(actual_Gradient[1] - GMLS_GradY) > failure_tolerance) {
350  all_passed = false;
351  std::cout << "Failed GradY by: " << std::abs(actual_Gradient[1] - GMLS_GradY) << std::endl;
352  }
353  }
354 
355  if (dimension>2) {
356  if(std::abs(actual_Gradient[2] - GMLS_GradZ) > failure_tolerance) {
357  all_passed = false;
358  std::cout << "Failed GradZ by: " << std::abs(actual_Gradient[2] - GMLS_GradZ) << std::endl;
359  }
360  }
361 
362  if(std::abs(actual_Divergence - GMLS_Divergence) > failure_tolerance) {
363  all_passed = false;
364  std::cout << "Failed Divergence by: " << std::abs(actual_Divergence - GMLS_Divergence) << std::endl;
365  }
366 
367  double tmp_diff = 0;
368  if (dimension>1)
369  tmp_diff += std::abs(actual_CurlX - GMLS_CurlX) + std::abs(actual_CurlY - GMLS_CurlY);
370  if (dimension>2)
371  tmp_diff += std::abs(actual_CurlZ - GMLS_CurlZ);
372  if(std::abs(tmp_diff) > failure_tolerance) {
373  all_passed = false;
374  std::cout << "Failed Curl by: " << std::abs(tmp_diff) << std::endl;
375  }
376  Kokkos::Profiling::popRegion();
377  }
378 
379 }
380 
381  Kokkos::finalize();
382 #ifdef COMPADRE_USE_MPI
383  MPI_Finalize();
384 #endif
385 
386 if(all_passed) {
387  fprintf(stdout, "Passed test \n");
388  return 0;
389 } else {
390  fprintf(stdout, "Failed test \n");
391  return -1;
392 }
393 }
Compadre::ScalarPointEvaluation
Point evaluation of a scalar.
Definition: Compadre_Operators.hpp:21
Compadre::Evaluator
Lightweight Evaluator Helper This class is a lightweight wrapper for extracting and applying all rele...
Definition: Compadre_Evaluator.hpp:123
Compadre::GradientOfScalarPointEvaluation
Point evaluation of the gradient of a scalar.
Definition: Compadre_Operators.hpp:30
Compadre::LaplacianOfScalarPointEvaluation
Point evaluation of the laplacian of a scalar (could be on a manifold or not)
Definition: Compadre_Operators.hpp:26
Compadre_GMLS.hpp
main
int main(int argc, char **argv)
Definition: Compadre_UnitTests.cpp:43
Compadre::CurlOfVectorPointEvaluation
Point evaluation of the curl of a vector (results in a vector)
Definition: Compadre_Operators.hpp:36
Compadre::Evaluator::applyAlphasToDataSingleComponentSingleTargetSite
double applyAlphasToDataSingleComponentSingleTargetSite(view_type_data sampling_input_data, const int column_of_input, TargetOperation lro, const int target_index, const int evaluation_site_local_index, const int output_component_axis_1, const int output_component_axis_2, const int input_component_axis_1, const int input_component_axis_2, bool scalar_as_vector_if_needed=true) const
Dot product of alphas with sampling data, FOR A SINGLE target_index, where sampling data is in a 1D/2...
Definition: Compadre_Evaluator.hpp:156
Compadre::GMLS::getNP
static KOKKOS_INLINE_FUNCTION int getNP(const int m, const int dimension=3, const ReconstructionSpace r_space=ReconstructionSpace::ScalarTaylorPolynomial)
Returns size of the basis for a given polynomial order and dimension General to dimension 1...
Definition: Compadre_GMLS.hpp:512
trueGradient
KOKKOS_INLINE_FUNCTION void trueGradient(double *ans, double x, double y, double z, int order, int dimension)
Definition: GMLS_Tutorial.hpp:77
Compadre_Evaluator.hpp
Compadre::DivergenceOfVectorPointEvaluation
Point evaluation of the divergence of a vector (results in a scalar)
Definition: Compadre_Operators.hpp:34
trueLaplacian
KOKKOS_INLINE_FUNCTION double trueLaplacian(double x, double y, double z, int order, int dimension)
Definition: GMLS_Tutorial.hpp:37
Compadre::GMLS
Generalized Moving Least Squares (GMLS)
Definition: Compadre_GMLS.hpp:40
curlTestSolution
KOKKOS_INLINE_FUNCTION double curlTestSolution(double x, double y, double z, int component, int dimension)
Definition: GMLS_Tutorial.hpp:222
divergenceTestSamples
KOKKOS_INLINE_FUNCTION double divergenceTestSamples(double x, double y, double z, int component, int dimension)
Definition: GMLS_Tutorial.hpp:196
GMLS_Tutorial.hpp
Compadre::GMLS::setProblemData
void setProblemData(view_type_1 neighbor_lists, view_type_2 source_coordinates, view_type_3 target_coordinates, view_type_4 epsilons)
Sets basic problem data (neighbor lists, source coordinates, and target coordinates) ...
Definition: Compadre_GMLS.hpp:849
CommandLineProcessor.hpp
trueSolution
KOKKOS_INLINE_FUNCTION double trueSolution(double x, double y, double z, int order, int dimension)
Definition: GMLS_Tutorial.hpp:22
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