feat: Merge Atomizer-Field neural network module into main repository
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>
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Antoine
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atomizer-field/tests/test_synthetic.py
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atomizer-field/tests/test_synthetic.py
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"""
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test_synthetic.py
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Synthetic tests with known analytical solutions
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Tests basic functionality without real FEA data:
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- Model can be created
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- Forward pass works
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- Loss functions compute correctly
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- Predictions have correct shape
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"""
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import torch
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import numpy as np
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import sys
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from pathlib import Path
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# Add parent directory to path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from neural_models.field_predictor import create_model
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from neural_models.physics_losses import create_loss_function
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from torch_geometric.data import Data
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def test_model_creation():
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"""
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Test 1: Can we create the model?
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Expected: Model instantiates with correct number of parameters
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"""
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print(" Creating GNN model...")
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config = {
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'node_feature_dim': 12,
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'edge_feature_dim': 5,
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'hidden_dim': 64,
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'num_layers': 4,
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'dropout': 0.1
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}
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model = create_model(config)
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# Count parameters
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num_params = sum(p.numel() for p in model.parameters())
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print(f" Model created: {num_params:,} parameters")
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return {
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'status': 'PASS',
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'message': f'Model created successfully ({num_params:,} params)',
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'metrics': {'parameters': num_params}
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}
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def test_forward_pass():
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"""
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Test 2: Can model process data?
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Expected: Forward pass completes without errors
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"""
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print(" Testing forward pass...")
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# Create model
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config = {
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'node_feature_dim': 12,
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'edge_feature_dim': 5,
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'hidden_dim': 64,
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'num_layers': 4,
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'dropout': 0.1
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}
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model = create_model(config)
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model.eval()
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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, 12) # Node features
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edge_index = torch.randint(0, num_nodes, (2, num_edges)) # 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, return_stress=True)
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# Check outputs
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assert 'displacement' in results, "Missing displacement output"
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assert 'stress' in results, "Missing stress output"
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assert 'von_mises' in results, "Missing von Mises output"
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# Check shapes
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assert results['displacement'].shape == (num_nodes, 6), f"Wrong displacement shape: {results['displacement'].shape}"
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assert results['stress'].shape == (num_nodes, 6), f"Wrong stress shape: {results['stress'].shape}"
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assert results['von_mises'].shape == (num_nodes,), f"Wrong von Mises shape: {results['von_mises'].shape}"
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print(f" Displacement shape: {results['displacement'].shape} [OK]")
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print(f" Stress shape: {results['stress'].shape} [OK]")
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print(f" Von Mises shape: {results['von_mises'].shape} [OK]")
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return {
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'status': 'PASS',
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'message': 'Forward pass successful',
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'metrics': {
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'num_nodes': num_nodes,
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'displacement_shape': list(results['displacement'].shape),
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'stress_shape': list(results['stress'].shape)
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}
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}
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def test_loss_computation():
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"""
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Test 3: Do loss functions work?
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Expected: All loss types compute without errors
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"""
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print(" Testing loss functions...")
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# Create dummy predictions and targets
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num_nodes = 100
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predictions = {
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'displacement': torch.randn(num_nodes, 6),
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'stress': torch.randn(num_nodes, 6),
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'von_mises': torch.abs(torch.randn(num_nodes))
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}
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targets = {
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'displacement': torch.randn(num_nodes, 6),
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'stress': torch.randn(num_nodes, 6)
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}
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loss_types = ['mse', 'relative', 'physics', 'max']
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loss_values = {}
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for loss_type in loss_types:
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loss_fn = create_loss_function(loss_type)
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losses = loss_fn(predictions, targets)
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assert 'total_loss' in losses, f"Missing total_loss for {loss_type}"
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assert not torch.isnan(losses['total_loss']), f"NaN loss for {loss_type}"
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assert not torch.isinf(losses['total_loss']), f"Inf loss for {loss_type}"
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loss_values[loss_type] = losses['total_loss'].item()
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print(f" {loss_type.upper()} loss: {loss_values[loss_type]:.6f} [OK]")
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return {
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'status': 'PASS',
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'message': 'All loss functions working',
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'metrics': loss_values
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}
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def test_batch_processing():
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"""
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Test 4: Can model handle batches?
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Expected: Batch processing works correctly
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"""
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print(" Testing batch processing...")
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config = {
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'node_feature_dim': 12,
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'edge_feature_dim': 5,
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'hidden_dim': 64,
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'num_layers': 4,
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'dropout': 0.1
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}
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model = create_model(config)
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model.eval()
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# Create batch of 3 graphs
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graphs = []
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for i in range(3):
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num_nodes = 50 + i * 10 # Different sizes
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num_edges = 150 + i * 30
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x = torch.randn(num_nodes, 12)
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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.full((num_nodes,), i, dtype=torch.long)
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graphs.append(Data(x=x, edge_index=edge_index, edge_attr=edge_attr, batch=batch))
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# Process batch
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total_nodes = sum(g.x.shape[0] for g in graphs)
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with torch.no_grad():
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for i, graph in enumerate(graphs):
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results = model(graph, return_stress=True)
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print(f" Graph {i+1}: {graph.x.shape[0]} nodes -> predictions [OK]")
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return {
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'status': 'PASS',
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'message': 'Batch processing successful',
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'metrics': {'num_graphs': len(graphs), 'total_nodes': total_nodes}
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}
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def test_gradient_flow():
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"""
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Test 5: Do gradients flow correctly?
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Expected: Gradients computed without errors
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"""
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print(" Testing gradient flow...")
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config = {
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'node_feature_dim': 12,
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'edge_feature_dim': 5,
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'hidden_dim': 64,
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'num_layers': 4,
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'dropout': 0.1
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}
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model = create_model(config)
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model.train()
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# Create dummy data
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num_nodes = 50
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num_edges = 150
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x = torch.randn(num_nodes, 12)
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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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# Forward pass
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results = model(data, return_stress=True)
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# Compute loss
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targets = {
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'displacement': torch.randn(num_nodes, 6),
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'stress': torch.randn(num_nodes, 6)
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}
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loss_fn = create_loss_function('mse')
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losses = loss_fn(results, targets)
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# Backward pass
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losses['total_loss'].backward()
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# Check gradients
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has_grad = sum(1 for p in model.parameters() if p.grad is not None)
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total_params = sum(1 for _ in model.parameters())
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print(f" Parameters with gradients: {has_grad}/{total_params} [OK]")
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assert has_grad == total_params, f"Not all parameters have gradients"
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return {
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'status': 'PASS',
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'message': 'Gradients computed successfully',
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'metrics': {
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'parameters_with_grad': has_grad,
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'total_parameters': total_params
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}
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}
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if __name__ == "__main__":
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print("\nRunning synthetic tests...\n")
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tests = [
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("Model Creation", test_model_creation),
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("Forward Pass", test_forward_pass),
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("Loss Computation", test_loss_computation),
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("Batch Processing", test_batch_processing),
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("Gradient Flow", test_gradient_flow)
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]
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passed = 0
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failed = 0
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for name, test_func in tests:
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print(f"[TEST] {name}")
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try:
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result = test_func()
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if result['status'] == 'PASS':
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print(f" [PASS]\n")
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passed += 1
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else:
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print(f" [FAIL]: {result['message']}\n")
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failed += 1
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except Exception as e:
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print(f" [FAIL]: {str(e)}\n")
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failed += 1
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print(f"\nResults: {passed} passed, {failed} failed")
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