add new function

NonLinearSolver/modelReduction/DeepMaterialNetworks.cpp

@@ -785,6 +785,10 @@ void BimaterialHomogenization::computeBimaterial(const fullMatrix<double>& C1, c
     bool ok = fullMatrixOperation::invertMatrix(A,invA,stiff,DinvADA);
     if (!ok)
     {
+      C1.print("C1");
+      C2.print("C2");
+      normal.print("normal");
+      printf("f1 = %e, f2 = %e\n",f1,f2);
       A.print("A");
     }
     //
@@ -1561,6 +1565,26 @@ void TrainingDeepMaterialNetwork::setTrainingSample(int row, const fullMatrix<do
   }
 };
 
+void TrainingDeepMaterialNetwork::setTrainingSample(int row, const fullMatrix<double>& C1, const fullMatrix<double>& C2,const fullMatrix<double>& C3, const fullMatrix<double>& C4, const fullMatrix<double>& Ceff)
+{
+  if (_homo->withPlaneStrain())
+  {
+    fullMatrixOperation::reducePlaneStrain(C1,_XTrain[row][0]);
+    fullMatrixOperation::reducePlaneStrain(C2,_XTrain[row][1]);
+    fullMatrixOperation::reducePlaneStrain(C3,_XTrain[row][2]);
+    fullMatrixOperation::reducePlaneStrain(C4,_XTrain[row][3]);
+    fullMatrixOperation::reducePlaneStrain(Ceff,_YTrain[row]);
+  }
+  else
+  {
+    fullMatrixOperation::reduceMat99ToMat66(C1,_XTrain[row][0]);
+    fullMatrixOperation::reduceMat99ToMat66(C2,_XTrain[row][1]);
+    fullMatrixOperation::reduceMat99ToMat66(C3,_XTrain[row][2]);
+    fullMatrixOperation::reduceMat99ToMat66(C4,_XTrain[row][3]);
+    fullMatrixOperation::reduceMat99ToMat66(Ceff,_YTrain[row]);
+  }
+};
+
 void TrainingDeepMaterialNetwork::setTestSample(int row, const fullMatrix<double>& C1, const fullMatrix<double>& Ceff)
 {
   static fullMatrix<double> C2;
@@ -1615,6 +1639,26 @@ void TrainingDeepMaterialNetwork::setTestSample(int row, const fullMatrix<double
   }
 };
 
+void TrainingDeepMaterialNetwork::setTestSample(int row, const fullMatrix<double>& C1, const fullMatrix<double>& C2, const fullMatrix<double>& C3, const fullMatrix<double>& C4, const fullMatrix<double>& Ceff)
+{
+  if (_homo->withPlaneStrain())
+  {
+    fullMatrixOperation::reducePlaneStrain(C1,_XTest[row][0]);
+    fullMatrixOperation::reducePlaneStrain(C2,_XTest[row][1]);
+    fullMatrixOperation::reducePlaneStrain(C3,_XTest[row][2]);
+    fullMatrixOperation::reducePlaneStrain(C4,_XTest[row][3]);
+    fullMatrixOperation::reducePlaneStrain(Ceff,_YTest[row]);
+  }
+  else
+  {
+    fullMatrixOperation::reduceMat99ToMat66(C1,_XTest[row][0]);
+    fullMatrixOperation::reduceMat99ToMat66(C2,_XTest[row][1]);
+    fullMatrixOperation::reduceMat99ToMat66(C3,_XTest[row][2]);
+    fullMatrixOperation::reduceMat99ToMat66(C4,_XTest[row][3]);
+    fullMatrixOperation::reduceMat99ToMat66(Ceff,_YTest[row]);
+  }
+};
+
 void TrainingDeepMaterialNetwork::testDataSize(int Nt, int numPhase)
 {
   printf("size of test set = %d numPhase = %d\n",Nt,numPhase);

NonLinearSolver/modelReduction/DeepMaterialNetworks.h

@@ -277,10 +277,12 @@ class TrainingDeepMaterialNetwork
     void setTrainingSample(int row, const fullMatrix<double>& C1, const fullMatrix<double>& Ceff);
     void setTrainingSample(int row, const fullMatrix<double>& C1, const fullMatrix<double>& C2, const fullMatrix<double>& Ceff);
     void setTrainingSample(int row, const fullMatrix<double>& C1, const fullMatrix<double>& C2, const fullMatrix<double>& C3, const fullMatrix<double>& Ceff);
+    void setTrainingSample(int row, const fullMatrix<double>& C1, const fullMatrix<double>& C2, const fullMatrix<double>& C3, const fullMatrix<double>& C4, const fullMatrix<double>& Ceff);
     void testDataSize(int Ns, int numPhase);
     void setTestSample(int row, const fullMatrix<double>& C1, const fullMatrix<double>& Ceff);
     void setTestSample(int row, const fullMatrix<double>& C1, const fullMatrix<double>& C2, const fullMatrix<double>& Ceff);
     void setTestSample(int row, const fullMatrix<double>& C1, const fullMatrix<double>& C2, const fullMatrix<double>& C3, const fullMatrix<double>& Ceff);
+    void setTestSample(int row, const fullMatrix<double>& C1, const fullMatrix<double>& C2, const fullMatrix<double>& C3, const fullMatrix<double>& C4, const fullMatrix<double>& Ceff);
     void train(double lr, int maxEpoch, 
               std::string loss = "mare", 
               std::string historyFileName="history.csv",