143 lines
4.5 KiB
Python
143 lines
4.5 KiB
Python
import torch
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import torch.nn as nn
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from torch_geometric.nn import MessagePassing
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from torch.nn.utils import spectral_norm
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class LogPhysEncoder(nn.Module):
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"""
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Processes 1/dx in log-space to handle multiple scales of geometry
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(from micro-meshes to macro-meshes) without numerical instability.
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"""
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def __init__(self, hidden_dim):
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super().__init__()
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self.mlp = nn.Sequential(
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spectral_norm(nn.Linear(1, hidden_dim)),
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nn.GELU(),
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spectral_norm(nn.Linear(hidden_dim, 1)),
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nn.Softplus(), # Physical conductance must be positive
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)
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def forward(self, inv_dx):
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# We use log(1/dx) to linearize the scale of different geometries
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log_inv_dx = torch.log(inv_dx + 1e-9)
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return self.mlp(log_inv_dx)
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class DiffusionLayer(MessagePassing):
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"""
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Modella: T_new = T_old + dt * Divergenza(Flusso)
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"""
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def __init__(
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self,
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channels: int,
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**kwargs,
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):
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super().__init__(aggr="add", **kwargs)
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self.conductivity_net = nn.Sequential(
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spectral_norm(nn.Linear(channels, channels, bias=False)),
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nn.GELU(),
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spectral_norm(nn.Linear(channels, channels, bias=False)),
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)
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self.phys_encoder = LogPhysEncoder(hidden_dim=channels)
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self.alpha_param = nn.Parameter(torch.tensor(1e-2))
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@property
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def alpha(self):
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return torch.clamp(self.alpha_param, min=1e-7, max=1.0)
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def forward(self, x, edge_index, edge_weight, conductivity):
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edge_weight = edge_weight.unsqueeze(-1)
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conductance = self.phys_encoder(edge_weight)
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net_flux = self.propagate(edge_index, x=x, conductance=conductance)
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return x + self.alpha * net_flux
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def message(self, x_i, x_j, conductance):
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delta = x_j - x_i
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flux = delta * conductance
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flux = flux + self.conductivity_net(flux)
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return flux
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class DiffusionNet(nn.Module):
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def __init__(
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self,
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input_dim=1,
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output_dim=1,
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hidden_dim=8,
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n_layers=4,
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shared_weights=False,
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):
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super().__init__()
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# Encoder: Projects input temperature to hidden feature space
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self.enc = nn.Sequential(
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spectral_norm(nn.Linear(input_dim, hidden_dim, bias=True)),
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nn.GELU(),
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spectral_norm(nn.Linear(hidden_dim, hidden_dim, bias=True)),
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)
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self.scale_x = nn.Parameter(torch.zeros(hidden_dim))
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# Scale parameters for conditioning
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self.scale_edge_attr = nn.Parameter(torch.zeros(1))
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# If shared_weights is True, use the same DiffusionLayer multiple times
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if shared_weights:
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diffusion_layer = DiffusionLayer(hidden_dim)
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self.layers = torch.nn.ModuleList(
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[diffusion_layer for _ in range(n_layers)]
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)
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# If shared_weights is False, use separate DiffusionLayers
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else:
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# Stack of Diffusion Layers
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self.layers = torch.nn.ModuleList(
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[DiffusionLayer(hidden_dim) for _ in range(n_layers)]
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)
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# Decoder: Projects hidden features back to Temperature space
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self.dec = nn.Sequential(
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spectral_norm(nn.Linear(hidden_dim, hidden_dim, bias=True)),
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nn.GELU(),
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spectral_norm(nn.Linear(hidden_dim, output_dim, bias=True)),
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nn.Softplus(), # Ensure positive temperature output
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)
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self.func = torch.nn.GELU()
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self.dt_param = nn.Parameter(torch.tensor(1e-2))
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@property
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def dt(self):
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return torch.clamp(self.dt_param, min=1e-5, max=0.5)
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def forward(self, x, edge_index, edge_attr, conductivity):
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# 1. Global Residual Connection setup
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# We save the input to add it back at the very end.
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# The network learns the correction (Delta T), not the absolute T.
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x_input = x
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# 2. Encode
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h = self.enc(x) * torch.exp(self.scale_x)
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# Scale edge attributes (learnable gating of physical conductivity)
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w = edge_attr * torch.exp(self.scale_edge_attr)
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# 4. Message Passing (Diffusion Steps)
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for layer in self.layers:
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# h is updated internally via residual connection in DiffusionLayer
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h = layer(h, edge_index, w, conductivity)
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h = self.func(h)
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# 5. Decode
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delta_x = self.dec(h)
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# 6. Final Update (Explicit Euler Step)
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# T_new = T_old + Correction
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return delta_x + x_input * self.dt
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# return delta_x
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