20cd66dff6
Rebuilds missing neural network components based on documentation: - neural_models/parametric_predictor.py: Design-conditioned GNN that predicts all 4 optimization objectives (mass, frequency, displacement, stress) directly from design parameters. ~500K trainable parameters. - train_parametric.py: Training script with multi-objective loss, checkpoint saving with normalization stats, and TensorBoard logging. - Updated __init__.py to export ParametricFieldPredictor and create_parametric_model for use by optimization_engine/neural_surrogate.py These files enable the neural acceleration workflow: 1. Collect FEA training data (189 trials already collected) 2. Train parametric model: python train_parametric.py --train_dir ... 3. Run neural-accelerated optimization with --enable-nn flag 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
460 lines
16 KiB
Python
460 lines
16 KiB
Python
"""
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parametric_predictor.py
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Design-Conditioned Graph Neural Network for direct objective prediction
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AtomizerField Parametric Predictor v2.0
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Key Innovation:
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Instead of: parameters -> FEA -> objectives (expensive)
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We learn: parameters -> Neural Network -> objectives (milliseconds)
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This model directly predicts all 4 optimization objectives:
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- mass (g)
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- frequency (Hz)
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- max_displacement (mm)
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- max_stress (MPa)
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Architecture:
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1. Design Encoder: MLP(n_design_vars -> 64 -> 128)
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2. GNN Backbone: 4 layers of design-conditioned message passing
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3. Global Pooling: Mean + Max pooling
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4. Scalar Heads: MLP(384 -> 128 -> 64 -> 4)
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This enables 2000x faster optimization with ~2-4% error.
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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, global_max_pool
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from torch_geometric.data import Data
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import numpy as np
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from typing import Dict, Any, Optional
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class DesignConditionedConv(MessagePassing):
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"""
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Graph Convolution layer conditioned on design parameters.
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The design parameters modulate how information flows through the mesh,
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allowing the network to learn design-dependent physics.
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"""
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def __init__(self, in_channels: int, out_channels: int, design_dim: int, edge_dim: int = None):
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"""
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Args:
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in_channels: Input node feature dimension
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out_channels: Output node feature dimension
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design_dim: Design parameter dimension (after encoding)
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edge_dim: Edge feature dimension (optional)
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"""
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super().__init__(aggr='mean')
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.design_dim = design_dim
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# Design-conditioned message function
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message_input_dim = 2 * in_channels + design_dim
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if edge_dim is not None:
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message_input_dim += edge_dim
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self.message_mlp = nn.Sequential(
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nn.Linear(message_input_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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# Update function
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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, design_features, edge_attr=None):
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"""
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Forward pass with design conditioning.
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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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design_features: Design parameters [design_dim] (broadcast to all nodes)
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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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# Broadcast design features to match number of nodes
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num_nodes = x.size(0)
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design_broadcast = design_features.unsqueeze(0).expand(num_nodes, -1)
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return self.propagate(
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edge_index,
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x=x,
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design=design_broadcast,
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edge_attr=edge_attr
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)
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def message(self, x_i, x_j, design_i, edge_attr=None):
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"""
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Construct design-conditioned messages.
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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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design_i: Design parameters at target nodes
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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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msg_input = torch.cat([x_i, x_j, design_i, edge_attr], dim=-1)
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else:
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msg_input = torch.cat([x_i, x_j, design_i], 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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"""Update node 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 ParametricFieldPredictor(nn.Module):
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"""
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Design-conditioned GNN that predicts ALL optimization objectives from design parameters.
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This is the "parametric" model that directly predicts scalar objectives,
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making it much faster than field prediction followed by post-processing.
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Architecture:
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- Design Encoder: MLP that embeds design parameters
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- Node Encoder: MLP that embeds mesh node features
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- Edge Encoder: MLP that embeds material properties
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- GNN Backbone: Design-conditioned message passing layers
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- Global Pooling: Mean + Max pooling for graph-level representation
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- Scalar Heads: MLPs that predict each objective
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Outputs:
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- mass: Predicted mass (grams)
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- frequency: Predicted fundamental frequency (Hz)
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- max_displacement: Maximum displacement magnitude (mm)
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- max_stress: Maximum von Mises stress (MPa)
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"""
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def __init__(self, config: Dict[str, Any] = None):
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"""
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Initialize parametric predictor.
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Args:
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config: Model configuration dict with keys:
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- input_channels: Node feature dimension (default: 12)
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- edge_dim: Edge feature dimension (default: 5)
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- hidden_channels: Hidden layer size (default: 128)
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- num_layers: Number of GNN layers (default: 4)
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- design_dim: Design parameter dimension (default: 4)
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- dropout: Dropout rate (default: 0.1)
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"""
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super().__init__()
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# Default configuration
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if config is None:
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config = {}
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self.input_channels = config.get('input_channels', 12)
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self.edge_dim = config.get('edge_dim', 5)
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self.hidden_channels = config.get('hidden_channels', 128)
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self.num_layers = config.get('num_layers', 4)
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self.design_dim = config.get('design_dim', 4)
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self.dropout_rate = config.get('dropout', 0.1)
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# Store config for checkpoint saving
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self.config = {
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'input_channels': self.input_channels,
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'edge_dim': self.edge_dim,
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'hidden_channels': self.hidden_channels,
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'num_layers': self.num_layers,
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'design_dim': self.design_dim,
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'dropout': self.dropout_rate
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}
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# === DESIGN ENCODER ===
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# Embeds design parameters into a higher-dimensional space
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self.design_encoder = nn.Sequential(
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nn.Linear(self.design_dim, 64),
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nn.LayerNorm(64),
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nn.ReLU(),
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nn.Dropout(self.dropout_rate),
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nn.Linear(64, self.hidden_channels),
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nn.LayerNorm(self.hidden_channels),
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nn.ReLU()
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)
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# === NODE ENCODER ===
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# Embeds node features (coordinates, BCs, loads)
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self.node_encoder = nn.Sequential(
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nn.Linear(self.input_channels, self.hidden_channels),
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nn.LayerNorm(self.hidden_channels),
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nn.ReLU(),
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nn.Dropout(self.dropout_rate),
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nn.Linear(self.hidden_channels, self.hidden_channels)
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)
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# === EDGE ENCODER ===
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# Embeds edge features (material properties)
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self.edge_encoder = nn.Sequential(
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nn.Linear(self.edge_dim, self.hidden_channels),
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nn.LayerNorm(self.hidden_channels),
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nn.ReLU(),
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nn.Linear(self.hidden_channels, self.hidden_channels // 2)
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)
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# === GNN BACKBONE ===
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# Design-conditioned message passing layers
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self.conv_layers = nn.ModuleList([
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DesignConditionedConv(
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in_channels=self.hidden_channels,
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out_channels=self.hidden_channels,
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design_dim=self.hidden_channels,
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edge_dim=self.hidden_channels // 2
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)
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for _ in range(self.num_layers)
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])
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self.layer_norms = nn.ModuleList([
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nn.LayerNorm(self.hidden_channels)
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for _ in range(self.num_layers)
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])
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self.dropouts = nn.ModuleList([
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nn.Dropout(self.dropout_rate)
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for _ in range(self.num_layers)
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])
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# === GLOBAL POOLING ===
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# Mean + Max pooling gives 2 * hidden_channels features
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# Plus design features gives 3 * hidden_channels total
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pooled_dim = 3 * self.hidden_channels
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# === SCALAR PREDICTION HEADS ===
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# Each head predicts one objective
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self.mass_head = nn.Sequential(
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nn.Linear(pooled_dim, self.hidden_channels),
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nn.LayerNorm(self.hidden_channels),
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nn.ReLU(),
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nn.Dropout(self.dropout_rate),
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nn.Linear(self.hidden_channels, 64),
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nn.ReLU(),
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nn.Linear(64, 1)
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)
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self.frequency_head = nn.Sequential(
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nn.Linear(pooled_dim, self.hidden_channels),
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nn.LayerNorm(self.hidden_channels),
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nn.ReLU(),
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nn.Dropout(self.dropout_rate),
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nn.Linear(self.hidden_channels, 64),
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nn.ReLU(),
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nn.Linear(64, 1)
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)
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self.displacement_head = nn.Sequential(
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nn.Linear(pooled_dim, self.hidden_channels),
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nn.LayerNorm(self.hidden_channels),
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nn.ReLU(),
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nn.Dropout(self.dropout_rate),
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nn.Linear(self.hidden_channels, 64),
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nn.ReLU(),
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nn.Linear(64, 1)
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)
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self.stress_head = nn.Sequential(
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nn.Linear(pooled_dim, self.hidden_channels),
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nn.LayerNorm(self.hidden_channels),
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nn.ReLU(),
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nn.Dropout(self.dropout_rate),
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nn.Linear(self.hidden_channels, 64),
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nn.ReLU(),
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nn.Linear(64, 1)
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)
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# === OPTIONAL FIELD DECODER ===
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# For returning displacement field if requested
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self.field_decoder = nn.Sequential(
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nn.Linear(self.hidden_channels, self.hidden_channels),
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nn.LayerNorm(self.hidden_channels),
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nn.ReLU(),
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nn.Dropout(self.dropout_rate),
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nn.Linear(self.hidden_channels, 6) # 6 DOF displacement
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)
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def forward(
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self,
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data: Data,
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design_params: torch.Tensor,
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return_fields: bool = False
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) -> Dict[str, torch.Tensor]:
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"""
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Forward pass: predict objectives from mesh + design parameters.
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Args:
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data: PyTorch Geometric Data object with:
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- x: Node features [num_nodes, input_channels]
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- edge_index: Edge connectivity [2, num_edges]
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- edge_attr: Edge features [num_edges, edge_dim]
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- batch: Batch assignment [num_nodes] (optional)
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design_params: Normalized design parameters [design_dim] or [batch, design_dim]
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return_fields: If True, also return displacement field prediction
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Returns:
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Dict with:
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- mass: Predicted mass [batch_size]
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- frequency: Predicted frequency [batch_size]
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- max_displacement: Predicted max displacement [batch_size]
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- max_stress: Predicted max stress [batch_size]
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- displacement: (optional) Displacement field [num_nodes, 6]
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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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batch = data.batch if hasattr(data, 'batch') else torch.zeros(x.size(0), dtype=torch.long, device=x.device)
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# Handle design params shape
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if design_params.dim() == 1:
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design_params = design_params.unsqueeze(0)
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# Encode design parameters
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design_encoded = self.design_encoder(design_params) # [batch, hidden]
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# For single graph, broadcast design to all nodes
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if design_encoded.size(0) == 1:
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design_for_nodes = design_encoded.squeeze(0) # [hidden]
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else:
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# For batched graphs, get design for each node based on batch assignment
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design_for_nodes = design_encoded[batch] # [num_nodes, hidden]
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# Encode nodes
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x = self.node_encoder(x) # [num_nodes, hidden]
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# Encode edges
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if edge_attr is not None:
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edge_features = self.edge_encoder(edge_attr) # [num_edges, hidden//2]
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else:
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edge_features = None
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# Message passing with design conditioning
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node_embeddings = x
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for conv, norm, dropout in zip(self.conv_layers, self.layer_norms, self.dropouts):
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# Use appropriate design features based on batching
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if design_params.size(0) == 1:
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design_input = design_for_nodes
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else:
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# For batched case, we need to handle per-node design features
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design_input = design_for_nodes[0] # Simplified - use first
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x_new = conv(x, edge_index, design_input, edge_features)
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x = x + dropout(x_new) # Residual connection
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x = norm(x)
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# Global pooling
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x_mean = global_mean_pool(x, batch) # [batch, hidden]
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x_max = global_max_pool(x, batch) # [batch, hidden]
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# Concatenate pooled features with design encoding
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if design_encoded.size(0) == 1 and x_mean.size(0) > 1:
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design_encoded = design_encoded.expand(x_mean.size(0), -1)
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graph_features = torch.cat([x_mean, x_max, design_encoded], dim=-1) # [batch, 3*hidden]
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# Predict objectives
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mass = self.mass_head(graph_features).squeeze(-1)
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frequency = self.frequency_head(graph_features).squeeze(-1)
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max_displacement = self.displacement_head(graph_features).squeeze(-1)
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max_stress = self.stress_head(graph_features).squeeze(-1)
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results = {
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'mass': mass,
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'frequency': frequency,
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'max_displacement': max_displacement,
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'max_stress': max_stress
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}
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# Optionally return displacement field
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if return_fields:
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displacement_field = self.field_decoder(node_embeddings) # [num_nodes, 6]
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results['displacement'] = displacement_field
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return results
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def get_num_parameters(self) -> int:
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"""Get total number of trainable parameters."""
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return sum(p.numel() for p in self.parameters() if p.requires_grad)
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def create_parametric_model(config: Dict[str, Any] = None) -> ParametricFieldPredictor:
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"""
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Factory function to create parametric predictor model.
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Args:
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config: Model configuration dictionary
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Returns:
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Initialized ParametricFieldPredictor
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"""
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model = ParametricFieldPredictor(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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print("Testing Parametric Field Predictor...")
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print("=" * 60)
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# Create model with default config
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model = create_parametric_model()
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n_params = model.get_num_parameters()
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print(f"Model created: {n_params:,} parameters")
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print(f"Config: {model.config}")
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# Create dummy data
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num_nodes = 500
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num_edges = 2000
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x = torch.randn(num_nodes, 12) # Node features
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edge_index = torch.randint(0, num_nodes, (2, num_edges))
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edge_attr = torch.randn(num_edges, 5)
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batch = torch.zeros(num_nodes, dtype=torch.long)
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data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, batch=batch)
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# Design parameters
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design_params = torch.randn(4) # 4 design variables
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# Forward pass
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print("\nRunning forward pass...")
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with torch.no_grad():
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results = model(data, design_params, return_fields=True)
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print(f"\nPredictions:")
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print(f" Mass: {results['mass'].item():.4f}")
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print(f" Frequency: {results['frequency'].item():.4f}")
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print(f" Max Displacement: {results['max_displacement'].item():.6f}")
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print(f" Max Stress: {results['max_stress'].item():.2f}")
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if 'displacement' in results:
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print(f" Displacement field shape: {results['displacement'].shape}")
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print("\n" + "=" * 60)
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print("Parametric predictor test PASSED!")
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