d5ffba099e
Permanently integrates the Atomizer-Field GNN surrogate system: - neural_models/: Graph Neural Network for FEA field prediction - batch_parser.py: Parse training data from FEA exports - train.py: Neural network training pipeline - predict.py: Inference engine for fast predictions This enables 600x-2200x speedup over traditional FEA by replacing expensive simulations with millisecond neural network predictions. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
491 lines
16 KiB
Python
491 lines
16 KiB
Python
"""
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field_predictor.py
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Graph Neural Network for predicting complete FEA field results
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AtomizerField Field Predictor v2.0
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Uses Graph Neural Networks to learn the physics of structural response.
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Key Innovation:
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Instead of: parameters → FEA → max_stress (scalar)
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We learn: parameters → Neural Network → complete stress field (N values)
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This enables 1000x faster optimization with physics understanding.
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch_geometric.nn import MessagePassing, global_mean_pool
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from torch_geometric.data import Data
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import numpy as np
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class MeshGraphConv(MessagePassing):
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"""
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Custom Graph Convolution for FEA meshes
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This layer propagates information along mesh edges (element connectivity)
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to learn how forces flow through the structure.
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Key insight: Stress and displacement fields follow mesh topology.
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Adjacent elements influence each other through equilibrium.
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"""
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def __init__(self, in_channels, out_channels, edge_dim=None):
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"""
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Args:
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in_channels (int): Input node feature dimension
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out_channels (int): Output node feature dimension
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edge_dim (int): Edge feature dimension (optional)
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"""
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super().__init__(aggr='mean') # Mean aggregation of neighbor messages
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self.in_channels = in_channels
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self.out_channels = out_channels
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# Message function: how to combine node and edge features
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if edge_dim is not None:
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self.message_mlp = nn.Sequential(
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nn.Linear(2 * in_channels + edge_dim, out_channels),
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nn.LayerNorm(out_channels),
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nn.ReLU(),
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nn.Linear(out_channels, out_channels)
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)
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else:
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self.message_mlp = nn.Sequential(
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nn.Linear(2 * in_channels, out_channels),
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nn.LayerNorm(out_channels),
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nn.ReLU(),
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nn.Linear(out_channels, out_channels)
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)
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# Update function: how to update node features
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self.update_mlp = nn.Sequential(
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nn.Linear(in_channels + out_channels, out_channels),
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nn.LayerNorm(out_channels),
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nn.ReLU(),
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nn.Linear(out_channels, out_channels)
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)
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self.edge_dim = edge_dim
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def forward(self, x, edge_index, edge_attr=None):
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"""
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Propagate messages through the mesh graph
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Args:
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x: Node features [num_nodes, in_channels]
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edge_index: Edge connectivity [2, num_edges]
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edge_attr: Edge features [num_edges, edge_dim] (optional)
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Returns:
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Updated node features [num_nodes, out_channels]
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"""
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return self.propagate(edge_index, x=x, edge_attr=edge_attr)
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def message(self, x_i, x_j, edge_attr=None):
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"""
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Construct messages from neighbors
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Args:
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x_i: Target node features
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x_j: Source node features
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edge_attr: Edge features
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"""
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if edge_attr is not None:
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# Combine source node, target node, and edge features
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msg_input = torch.cat([x_i, x_j, edge_attr], dim=-1)
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else:
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msg_input = torch.cat([x_i, x_j], dim=-1)
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return self.message_mlp(msg_input)
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def update(self, aggr_out, x):
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"""
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Update node features with aggregated messages
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Args:
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aggr_out: Aggregated messages from neighbors
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x: Original node features
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"""
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# Combine original features with aggregated messages
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update_input = torch.cat([x, aggr_out], dim=-1)
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return self.update_mlp(update_input)
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class FieldPredictorGNN(nn.Module):
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"""
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Graph Neural Network for predicting complete FEA fields
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Architecture:
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1. Node Encoder: Encode node positions, BCs, loads
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2. Edge Encoder: Encode element connectivity, material properties
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3. Message Passing: Propagate information through mesh (multiple layers)
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4. Field Decoder: Predict displacement/stress at each node/element
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This architecture respects physics:
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- Uses mesh topology (forces flow through connected elements)
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- Incorporates boundary conditions (fixed/loaded nodes)
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- Learns material behavior (E, nu → stress-strain relationship)
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"""
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def __init__(
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self,
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node_feature_dim=3, # Node coordinates (x, y, z)
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edge_feature_dim=5, # Material properties (E, nu, rho, etc.)
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hidden_dim=128,
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num_layers=6,
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output_dim=6, # 6 DOF displacement (3 translation + 3 rotation)
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dropout=0.1
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):
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"""
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Initialize field predictor
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Args:
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node_feature_dim (int): Dimension of node features (position + BCs + loads)
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edge_feature_dim (int): Dimension of edge features (material properties)
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hidden_dim (int): Hidden layer dimension
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num_layers (int): Number of message passing layers
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output_dim (int): Output dimension per node (6 for displacement)
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dropout (float): Dropout rate
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"""
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super().__init__()
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self.node_feature_dim = node_feature_dim
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self.edge_feature_dim = edge_feature_dim
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self.hidden_dim = hidden_dim
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self.num_layers = num_layers
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self.output_dim = output_dim
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# Node encoder: embed node coordinates + BCs + loads
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self.node_encoder = nn.Sequential(
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nn.Linear(node_feature_dim, hidden_dim),
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nn.LayerNorm(hidden_dim),
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nn.ReLU(),
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nn.Dropout(dropout),
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nn.Linear(hidden_dim, hidden_dim)
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)
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# Edge encoder: embed material properties
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self.edge_encoder = nn.Sequential(
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nn.Linear(edge_feature_dim, hidden_dim),
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nn.LayerNorm(hidden_dim),
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nn.ReLU(),
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nn.Linear(hidden_dim, hidden_dim // 2)
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)
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# Message passing layers (the physics learning happens here)
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self.conv_layers = nn.ModuleList([
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MeshGraphConv(
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in_channels=hidden_dim,
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out_channels=hidden_dim,
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edge_dim=hidden_dim // 2
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)
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for _ in range(num_layers)
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])
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self.layer_norms = nn.ModuleList([
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nn.LayerNorm(hidden_dim)
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for _ in range(num_layers)
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])
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self.dropouts = nn.ModuleList([
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nn.Dropout(dropout)
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for _ in range(num_layers)
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])
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# Field decoder: predict displacement at each node
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self.field_decoder = nn.Sequential(
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nn.Linear(hidden_dim, hidden_dim),
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nn.LayerNorm(hidden_dim),
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nn.ReLU(),
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nn.Dropout(dropout),
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nn.Linear(hidden_dim, hidden_dim // 2),
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nn.ReLU(),
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nn.Linear(hidden_dim // 2, output_dim)
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)
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# Physics-informed constraint layer (optional, ensures equilibrium)
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self.physics_scale = nn.Parameter(torch.ones(1))
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def forward(self, data):
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"""
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Forward pass: mesh → displacement field
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Args:
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data (torch_geometric.data.Data): Batch of mesh graphs containing:
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- x: Node features [num_nodes, node_feature_dim]
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- edge_index: Connectivity [2, num_edges]
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- edge_attr: Edge features [num_edges, edge_feature_dim]
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- batch: Batch assignment [num_nodes]
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Returns:
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displacement_field: Predicted displacement [num_nodes, output_dim]
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"""
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x, edge_index, edge_attr = data.x, data.edge_index, data.edge_attr
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# Encode nodes (positions + BCs + loads)
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x = self.node_encoder(x) # [num_nodes, hidden_dim]
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# Encode edges (material properties)
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if edge_attr is not None:
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edge_features = self.edge_encoder(edge_attr) # [num_edges, hidden_dim//2]
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else:
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edge_features = None
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# Message passing: learn how forces propagate through mesh
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for i, (conv, norm, dropout) in enumerate(zip(
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self.conv_layers, self.layer_norms, self.dropouts
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)):
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# Graph convolution
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x_new = conv(x, edge_index, edge_features)
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# Residual connection (helps gradients flow)
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x = x + dropout(x_new)
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# Layer normalization
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x = norm(x)
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# Decode to displacement field
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displacement = self.field_decoder(x) # [num_nodes, output_dim]
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# Apply physics-informed scaling
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displacement = displacement * self.physics_scale
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return displacement
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def predict_stress_from_displacement(self, displacement, data, material_props):
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"""
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Convert predicted displacement to stress using constitutive law
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This implements: σ = C : ε = C : (∇u)
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Where C is the material stiffness matrix
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Args:
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displacement: Predicted displacement [num_nodes, 6]
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data: Mesh graph data
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material_props: Material properties (E, nu)
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Returns:
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stress_field: Predicted stress [num_elements, n_components]
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"""
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# This would compute strain from displacement gradients
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# then apply material constitutive law
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# For now, we'll predict displacement and train a separate stress predictor
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raise NotImplementedError("Stress prediction implemented in StressPredictor")
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class StressPredictor(nn.Module):
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"""
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Predicts stress field from displacement field
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This can be:
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1. Physics-based: Compute strain from displacement, apply constitutive law
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2. Learned: Train neural network to predict stress from displacement
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We use learned approach for flexibility with nonlinear materials.
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"""
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def __init__(self, displacement_dim=6, hidden_dim=128, stress_components=6):
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"""
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Args:
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displacement_dim (int): Displacement DOFs per node
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hidden_dim (int): Hidden layer size
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stress_components (int): Stress tensor components (6 for 3D)
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"""
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super().__init__()
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# Stress predictor network
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self.stress_net = nn.Sequential(
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nn.Linear(displacement_dim, hidden_dim),
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nn.LayerNorm(hidden_dim),
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nn.ReLU(),
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nn.Linear(hidden_dim, hidden_dim),
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nn.LayerNorm(hidden_dim),
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nn.ReLU(),
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nn.Linear(hidden_dim, stress_components)
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)
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def forward(self, displacement):
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"""
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Predict stress from displacement
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Args:
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displacement: [num_nodes, displacement_dim]
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Returns:
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stress: [num_nodes, stress_components]
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"""
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return self.stress_net(displacement)
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class AtomizerFieldModel(nn.Module):
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"""
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Complete AtomizerField model: predicts both displacement and stress fields
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This is the main model you'll use for training and inference.
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"""
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def __init__(
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self,
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node_feature_dim=10, # 3 (xyz) + 6 (BC DOFs) + 1 (load magnitude)
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edge_feature_dim=5, # E, nu, rho, G, alpha
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hidden_dim=128,
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num_layers=6,
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dropout=0.1
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):
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"""
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Initialize complete field prediction model
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Args:
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node_feature_dim (int): Node features (coords + BCs + loads)
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edge_feature_dim (int): Edge features (material properties)
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hidden_dim (int): Hidden dimension
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num_layers (int): Message passing layers
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dropout (float): Dropout rate
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"""
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super().__init__()
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# Displacement predictor (main GNN)
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self.displacement_predictor = FieldPredictorGNN(
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node_feature_dim=node_feature_dim,
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edge_feature_dim=edge_feature_dim,
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hidden_dim=hidden_dim,
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num_layers=num_layers,
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output_dim=6, # 6 DOF displacement
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dropout=dropout
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)
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# Stress predictor (from displacement)
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self.stress_predictor = StressPredictor(
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displacement_dim=6,
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hidden_dim=hidden_dim,
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stress_components=6 # σxx, σyy, σzz, τxy, τyz, τxz
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)
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def forward(self, data, return_stress=True):
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"""
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Predict displacement and stress fields
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Args:
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data: Mesh graph data
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return_stress (bool): Whether to predict stress
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Returns:
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dict with:
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- displacement: [num_nodes, 6]
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- stress: [num_nodes, 6] (if return_stress=True)
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- von_mises: [num_nodes] (if return_stress=True)
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"""
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# Predict displacement
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displacement = self.displacement_predictor(data)
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results = {'displacement': displacement}
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if return_stress:
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# Predict stress from displacement
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stress = self.stress_predictor(displacement)
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# Calculate von Mises stress
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# σ_vm = sqrt(0.5 * ((σxx-σyy)² + (σyy-σzz)² + (σzz-σxx)² + 6(τxy² + τyz² + τxz²)))
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sxx, syy, szz, txy, tyz, txz = stress[:, 0], stress[:, 1], stress[:, 2], \
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stress[:, 3], stress[:, 4], stress[:, 5]
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von_mises = torch.sqrt(
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0.5 * (
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(sxx - syy)**2 + (syy - szz)**2 + (szz - sxx)**2 +
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6 * (txy**2 + tyz**2 + txz**2)
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)
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)
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results['stress'] = stress
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results['von_mises'] = von_mises
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return results
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def get_max_values(self, results):
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"""
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Extract maximum values (for compatibility with scalar optimization)
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Args:
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results: Output from forward()
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Returns:
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dict with max_displacement, max_stress
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"""
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max_displacement = torch.max(torch.norm(results['displacement'][:, :3], dim=1))
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max_stress = torch.max(results['von_mises']) if 'von_mises' in results else None
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return {
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'max_displacement': max_displacement,
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'max_stress': max_stress
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}
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def create_model(config=None):
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"""
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Factory function to create AtomizerField model
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Args:
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config (dict): Model configuration
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Returns:
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AtomizerFieldModel instance
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"""
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if config is None:
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config = {
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'node_feature_dim': 10,
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'edge_feature_dim': 5,
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'hidden_dim': 128,
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'num_layers': 6,
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'dropout': 0.1
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}
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model = AtomizerFieldModel(**config)
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# Initialize weights
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def init_weights(m):
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if isinstance(m, nn.Linear):
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nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='relu')
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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model.apply(init_weights)
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return model
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if __name__ == "__main__":
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# Test model creation
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print("Testing AtomizerField Model Creation...")
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model = create_model()
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print(f"Model created: {sum(p.numel() for p in model.parameters()):,} parameters")
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# Create dummy data
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num_nodes = 100
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num_edges = 300
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x = torch.randn(num_nodes, 10) # Node features
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edge_index = torch.randint(0, num_nodes, (2, num_edges)) # Edge connectivity
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edge_attr = torch.randn(num_edges, 5) # Edge features
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batch = torch.zeros(num_nodes, dtype=torch.long) # Batch assignment
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data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, batch=batch)
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# Forward pass
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with torch.no_grad():
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results = model(data)
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print(f"\nTest forward pass:")
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print(f" Displacement shape: {results['displacement'].shape}")
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print(f" Stress shape: {results['stress'].shape}")
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print(f" Von Mises shape: {results['von_mises'].shape}")
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max_vals = model.get_max_values(results)
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print(f"\nMax values:")
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print(f" Max displacement: {max_vals['max_displacement']:.6f}")
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print(f" Max stress: {max_vals['max_stress']:.2f}")
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print("\nModel test passed!")
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