PINN: allinea output a results/pinn/ e centralizza parametri in config

- visualizer.py: sostituisce animations/ con results/pinn/TIMESTAMP/,
  nomi fissi (heatmap.html, animation.html, comparison.html) come FDM
- config.py: aggiunge sezioni architettura, sampling, Adam, L-BFGS, loss weights
- model.py: costruisce HeatPINN dinamicamente da HIDDEN_SIZE/N_HIDDEN_LAYERS;
  heat_pinn_loss legge pesi W_PDE/W_IC/W_BC da config
- engine.py: tutti i parametri di training letti da config

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

engine.py

@@ -35,7 +35,10 @@ def _get_device():
     return torch.device('cpu')
 
 
-def prepare_data(N_f=4000, N_ic=400, N_bc=400):
+def prepare_data(N_f=None, N_ic=None, N_bc=None):
+    if N_f  is None: N_f  = config.N_F
+    if N_ic is None: N_ic = config.N_IC
+    if N_bc is None: N_bc = config.N_BC
     set_seed(42)
     device = _get_device()
 
@@ -61,11 +64,16 @@ def prepare_data(N_f=4000, N_ic=400, N_bc=400):
     return {'device': device, 'x_f': x_f, 't_f': t_f, 'x_ic': x_ic, 't_bc': t_bc}
 
 
-def train_model(data, epochs=5000, patience=100):
+def train_model(data, epochs=None, patience=None):
+    if epochs  is None: epochs  = config.EPOCHS
+    if patience is None: patience = config.PATIENCE
     device = data['device']
     model = HeatPINN().to(device)
-    optimizer = optim.Adam(model.parameters(), lr=1e-3)
-    scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=30, min_lr=1e-6)
+    optimizer = optim.Adam(model.parameters(), lr=config.LR_ADAM)
+    scheduler = ReduceLROnPlateau(optimizer, mode='min',
+                                  factor=config.SCHED_FACTOR,
+                                  patience=config.SCHED_PATIENCE,
+                                  min_lr=config.SCHED_MIN_LR)
 
     os.makedirs(MODELS_DIR, exist_ok=True)
     best_loss = float('inf')
@@ -104,12 +112,12 @@ def train_model(data, epochs=5000, patience=100):
     ckpt = torch.load(MODEL_SAVE_PATH, map_location=device)
     model.load_state_dict(ckpt['state_dict'])
 
-    lbfgs = optim.LBFGS(model.parameters(), lr=0.1, max_iter=50,
+    lbfgs = optim.LBFGS(model.parameters(), lr=config.LR_LBFGS, max_iter=50,
                          history_size=50, tolerance_grad=1e-7, line_search_fn='strong_wolfe')
 
     _last = {}
 
-    for step in range(20):
+    for step in range(config.LBFGS_STEPS):
         def closure():
             lbfgs.zero_grad()
             loss, L_pde, L_ic, L_bc = heat_pinn_loss(
@@ -127,7 +135,7 @@ def train_model(data, epochs=5000, patience=100):
             best_loss = _last['loss']
             torch.save({'state_dict': model.state_dict()}, MODEL_SAVE_PATH)
         if (step + 1) % 5 == 0:
-            print(f"L-BFGS step {step+1}/20 | Loss: {_last['loss']:.6f} "
+            print(f"L-BFGS step {step+1}/{config.LBFGS_STEPS} | Loss: {_last['loss']:.6f} "
                   f"| PDE: {_last['pde']:.6f} | IC: {_last['ic']:.6f} | BC: {_last['bc']:.6f}")
 
     print("Training complete! Model saved.")