2d example

DeepINN/domain/standard.py

@@ -16,10 +16,9 @@ def __init__(self, pde_equation, collocation_object, bc_object) -> None:
         super().__init__()
 
         self.pde = pde_equation
-        self.pde_sampler = collocation_object
+        self.pde_sampler = collocation_object # class of the PDE
         # if the bcs is not list, then make it a list
-        self.bc_sampler = bc_object if isinstance(bc_object, (list)) else [bc_object]
-
+        self.bc_sampler = bc_object if isinstance(bc_object, (list)) else [bc_object] # class of the boundary condition
         self.bc_list_len = len(self.bc_sampler)
 
     def sample_collocation_points(self):

DeepINN/model.py

@@ -28,10 +28,10 @@ def compile(self, optimiser_string : str, lr : float, metrics_string : str, devi
 
     def compile_domain(self):
         # sample collocation points
-        self.collocation_point_sample, self.collocation_point_labels = self.domain.sample_collocation_labels()
+        self.collocation_point_sample, self.collocation_point_labels = self.domain.sample_collocation_labels() # list of collocation points and their labels both as tensors
 
         # sample boundary points
-        self.boundary_point_sample, self.boundary_point_labels = self.domain.sample_boundary_labels()
+        self.boundary_point_sample, self.boundary_point_labels = self.domain.sample_boundary_labels() # list of boundary points and their labels both as tensors
         print("Domain compiled", file=sys.stderr, flush=True)
 
     def compile_network(self):
@@ -61,6 +61,9 @@ def initialise_training(self, iterations : int = None):
             if self.boundary_point_sample[0].size()[0] == 1: # if row is 1 in the particular boundary tensor
                 self.boundary_point_sample = torch.cat(self.boundary_point_sample, dim=0)
                 self.boundary_point_labels = torch.cat(self.boundary_point_labels, dim=0)
+            else:
+                self.boundary_point_sample = torch.cat(self.boundary_point_sample, dim=0)
+                self.boundary_point_labels = torch.cat(self.boundary_point_labels, dim=0)
 
             # Set requires_grad=True for self.collocation_point_sample
             self.collocation_point_sample.requires_grad = True