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PINN: normalizza input in [0,1]² e ottimizza autograd

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- forward(): divide (x,t) per (L, T_END) prima di passare alla rete,
  così le due dimensioni hanno la stessa scala indipendentemente da T_END
- heat_pinn_loss: calcola dT_dt e dT_dx in un singolo backward pass
  usando autograd.grad con lista [t_f, x_f]

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Davide Grilli
2026-05-14 14:20:20 +02:00
parent fbb0458f69
commit 98bfc78651
1 changed files with 6 additions and 5 deletions
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model.py
+6 -5
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@@ -13,11 +13,13 @@ class HeatPINN(nn.Module):
layers.append(nn.Linear(h, 1)) layers.append(nn.Linear(h, 1))
self.net = nn.Sequential(*layers) self.net = nn.Sequential(*layers)
def forward(self, x): def forward(self, xt):
# Normalize inputs to [0,1]x[0,1] so both dimensions share the same scale
scale = torch.tensor([config.L, config.T_END], device=xt.device, dtype=xt.dtype)
xt_norm = xt / scale
# Output scaled to physical range: T_AMB + (Q*L/K) * net # Output scaled to physical range: T_AMB + (Q*L/K) * net
# net learns dimensionless perturbation in [0,1] range # net learns dimensionless perturbation in [0,1] range
T_scale = config.T_AMB + (config.Q_VAL * config.L / config.K) * self.net(x) return config.T_AMB + (config.Q_VAL * config.L / config.K) * self.net(xt_norm)
return T_scale
def heat_pinn_loss(model, x_f, t_f, x_ic, t_bc, def heat_pinn_loss(model, x_f, t_f, x_ic, t_bc,
@@ -33,8 +35,7 @@ def heat_pinn_loss(model, x_f, t_f, x_ic, t_bc,
x_f = x_f.detach().requires_grad_(True) x_f = x_f.detach().requires_grad_(True)
t_f = t_f.detach().requires_grad_(True) t_f = t_f.detach().requires_grad_(True)
T_f = model(torch.stack([x_f, t_f], dim=1)) T_f = model(torch.stack([x_f, t_f], dim=1))
dT_dt = torch.autograd.grad(T_f.sum(), t_f, create_graph=True)[0] dT_dt, dT_dx = torch.autograd.grad(T_f.sum(), [t_f, x_f], create_graph=True)
dT_dx = torch.autograd.grad(T_f.sum(), x_f, create_graph=True)[0]
d2T_dx2 = torch.autograd.grad(dT_dx.sum(), x_f, create_graph=True)[0] d2T_dx2 = torch.autograd.grad(dT_dx.sum(), x_f, create_graph=True)[0]
sigma = 0.02 sigma = 0.02
Q_t_f = torch.where(t_f >= config.T_STEP, Q_t_f = torch.where(t_f >= config.T_STEP,