diff --git a/model.py b/model.py
index 08b3c0e..47bfb5e 100644
--- a/model.py
+++ b/model.py
@@ -22,10 +22,12 @@ class HeatPINN(nn.Module):
 
 # Precomputed loss scales (depend only on config constants)
 _T_char    = config.Q_VAL * config.L / config.K   # ~150 °C — temperature scale
-_grad_char = (config.Q_VAL / config.K) ** 2        # ~22500  — gradient scale²
 # _pde_scale covers both dT/dt and the Gaussian source peak (dominant with small sigma)
 _src_peak  = config.ALPHA * config.Q_VAL / (config.K * config.GAUSS_SIGMA * (2 * 3.141592653589793) ** 0.5)
 _pde_scale = max((_T_char / config.T_END) ** 2, _src_peak ** 2) + 1e-8
+# Robin BC residual scale: max(dT/dx, H_CONV/K * T_char) — convective term dominates when H*L/K >> 1
+_bc_scale  = max(config.Q_VAL / config.K,
+                 config.H_CONV * _T_char / config.K) ** 2
 
 
 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,
     if w_pde is None: w_pde = config.W_PDE
     if w_ic  is None: w_ic  = config.W_IC
     if w_bc  is None: w_bc  = config.W_BC
-    T_char    = _T_char
-    grad_char = _grad_char
+    T_char = _T_char
 
     # PDE residual: dT/dt - alpha * d2T/dx2 - source(x,t) = 0  (normalized by T_char/t_char)
     x_f = x_f.detach().requires_grad_(True)
@@ -59,13 +60,13 @@ def heat_pinn_loss(model, x_f, t_f, x_ic, t_bc,
     x_left = torch.zeros(t_bc.shape[0], device=t_bc.device).requires_grad_(True)
     T_left = model(torch.stack([x_left, t_bc.detach()], dim=1))
     dT_dx_left = torch.autograd.grad(T_left.sum(), x_left, create_graph=True)[0]
-    L_bc_left = ((dT_dx_left + (config.H_CONV / config.K) * (T_left.squeeze() - config.T_AMB)) ** 2).mean() / grad_char
+    L_bc_left = ((dT_dx_left + (config.H_CONV / config.K) * (T_left.squeeze() - config.T_AMB)) ** 2).mean() / _bc_scale
 
     # BC x=L: Robin — dT/dx + H_CONV/K * (T(L,t) - T_AMB) = 0
     x_right = torch.full((t_bc.shape[0],), config.L, device=t_bc.device).requires_grad_(True)
     T_right = model(torch.stack([x_right, t_bc.detach()], dim=1))
     dT_dx_right = torch.autograd.grad(T_right.sum(), x_right, create_graph=True)[0]
-    L_bc_right = ((dT_dx_right + (config.H_CONV / config.K) * (T_right.squeeze() - config.T_AMB)) ** 2).mean() / grad_char
+    L_bc_right = ((dT_dx_right + (config.H_CONV / config.K) * (T_right.squeeze() - config.T_AMB)) ** 2).mean() / _bc_scale
 
     L_bc = L_bc_left + L_bc_right
     total = w_pde * L_pde + w_ic * L_ic + w_bc * L_bc