103 lines
3.1 KiB
Python
103 lines
3.1 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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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.dt = nn.Parameter(torch.tensor(1e-4))
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self.conductivity_net = nn.Sequential(
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nn.Linear(channels, channels, bias=False),
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nn.GELU(),
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nn.Linear(channels, channels, bias=False),
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)
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self.phys_encoder = nn.Sequential(
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nn.Linear(1, 8, bias=False),
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nn.Tanh(),
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nn.Linear(8, 1, bias=False),
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nn.Softplus(),
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)
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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 + ((net_flux) * self.dt)
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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__(self, input_dim=1, output_dim=1, hidden_dim=8, n_layers=4):
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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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nn.Linear(input_dim, hidden_dim, bias=True),
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nn.GELU(),
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nn.Linear(hidden_dim, hidden_dim, bias=True),
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nn.GELU(),
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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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# 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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nn.Linear(hidden_dim, hidden_dim, bias=True),
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nn.GELU(),
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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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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 x_input + delta_x
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return delta_x
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