minor

ThermalSolver/data_module.py

@@ -40,6 +40,9 @@ class GraphDataModule(LightningDataModule):
         pos = torch.tensor(self.geometry["points"][0], dtype=torch.float32)[
             :, :2
         ]
+        bottom_boundary_ids = torch.tensor(
+            self.geometry["bottom_boundary_ids"][0], dtype=torch.int64
+        )
         self.data = [
             self._build_dataset(
                 torch.tensor(snapshot["conductivity"], dtype=torch.float32),
@@ -47,6 +50,7 @@ class GraphDataModule(LightningDataModule):
                 torch.tensor(snapshot["temperature"], dtype=torch.float32),
                 edge_index.T,
                 pos,
+                bottom_boundary_ids,
             )
             for snapshot in tqdm(hf_dataset, desc="Building graphs")
         ]
@@ -58,13 +62,15 @@ class GraphDataModule(LightningDataModule):
         temperature: torch.Tensor,
         edge_index: torch.Tensor,
         pos: torch.Tensor,
+        bottom_boundary_ids: torch.Tensor,
     ) -> Data:
         edge_index = to_undirected(edge_index, num_nodes=pos.size(0))
         edge_attr = pos[edge_index[0]] - pos[edge_index[1]]
         edge_attr = torch.cat(
             [edge_attr, torch.norm(edge_attr, dim=1).unsqueeze(-1)], dim=1
         )
-
+        boundary_temperature = boundary_vales[bottom_boundary_ids].max()
+        boundary_vales[bottom_boundary_ids] = 1.0
         return Data(
             x=boundary_vales.unsqueeze(-1),
             c=conductivity.unsqueeze(-1),
@@ -72,6 +78,7 @@ class GraphDataModule(LightningDataModule):
             pos=pos,
             edge_attr=edge_attr,
             y=temperature.unsqueeze(-1),
+            boundary_temperature=boundary_vales[bottom_boundary_ids].max(),
         )
 
     def setup(self, stage: str = None):

ThermalSolver/module.py

@@ -50,7 +50,10 @@ class GraphSolver(LightningModule):
         for _ in range(self.unrolling_steps):
             x_prev = x.detach()
             x = self(x_prev, c, edge_index=edge_index, edge_attr=edge_attr)
-            loss += self.loss(x, y)
+            actual_loss = self.loss(x, y)
+            loss += actual_loss
+            print(f"Train step loss: {actual_loss.item()}")
+
         self._log_loss(loss, batch, "train")
         return loss
 
@@ -78,3 +81,7 @@ class GraphSolver(LightningModule):
     def configure_optimizers(self):
         optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
         return optimizer
+
+    def scale_bc(self, data: Batch, y: torch.Tensor):
+        t = data.boundary_temperature[data.batch]
+        return y * t