PINN: sposta model.train() fuori dal loop e aggiunge weights_only a torch.load

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

engine.py

@@ -80,8 +80,8 @@ def train_model(data, epochs=None, patience=None):
     patience_counter = 0
 
     print(f"\n--- Heat PINN Training (Adam) on {device} ---")
+    model.train()
     for epoch in range(epochs):
-        model.train()
         optimizer.zero_grad()
         loss, L_pde, L_ic, L_bc = heat_pinn_loss(
             model, data['x_f'], data['t_f'], data['x_ic'], data['t_bc']
@@ -109,7 +109,7 @@ def train_model(data, epochs=None, patience=None):
 
     # L-BFGS fine-tuning phase (standard PINN practice for convergence to better minima)
     print("\n--- L-BFGS fine-tuning ---")
-    ckpt = torch.load(MODEL_SAVE_PATH, map_location=device)
+    ckpt = torch.load(MODEL_SAVE_PATH, map_location=device, weights_only=True)
     model.load_state_dict(ckpt['state_dict'])
 
     lbfgs = optim.LBFGS(model.parameters(), lr=config.LR_LBFGS, max_iter=50,
@@ -145,7 +145,7 @@ def _load_model(device):
     if not os.path.exists(MODEL_SAVE_PATH):
         print("Error: model not found. Train the model first.")
         return None
-    ckpt = torch.load(MODEL_SAVE_PATH, map_location=device)
+    ckpt = torch.load(MODEL_SAVE_PATH, map_location=device, weights_only=True)
     model = HeatPINN().to(device)
     model.load_state_dict(ckpt['state_dict'])
     model.eval()