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>
422 lines
13 KiB
Python
422 lines
13 KiB
Python
"""
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optimization_interface.py
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Bridge between AtomizerField neural network and Atomizer optimization platform
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AtomizerField Optimization Interface v2.1
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Enables gradient-based optimization with neural field predictions.
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Key Features:
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- Drop-in replacement for FEA evaluation (1000× faster)
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- Gradient computation for sensitivity analysis
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- Field-aware optimization (knows WHERE stress occurs)
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- Uncertainty quantification (knows when to trust predictions)
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- Automatic FEA fallback for high-uncertainty cases
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"""
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import torch
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import torch.nn.functional as F
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import numpy as np
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from pathlib import Path
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import json
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import time
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from neural_models.field_predictor import AtomizerFieldModel
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from neural_models.data_loader import FEAMeshDataset
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class NeuralFieldOptimizer:
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"""
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Optimization interface for AtomizerField
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This class provides a simple API for optimization:
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- evaluate(parameters) → objectives (max_stress, max_disp, etc.)
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- get_sensitivities(parameters) → gradients for optimization
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- get_fields(parameters) → complete stress/displacement fields
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Usage:
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optimizer = NeuralFieldOptimizer('checkpoint_best.pt')
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results = optimizer.evaluate(parameters)
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print(f"Max stress: {results['max_stress']:.2f} MPa")
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"""
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def __init__(
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self,
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model_path,
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uncertainty_threshold=0.1,
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enable_gradients=True,
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device=None
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):
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"""
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Initialize optimizer
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Args:
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model_path (str): Path to trained model checkpoint
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uncertainty_threshold (float): Uncertainty above which to recommend FEA
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enable_gradients (bool): Enable gradient computation
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device (str): Device to run on ('cuda' or 'cpu')
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"""
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if device is None:
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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else:
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self.device = torch.device(device)
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print(f"\nAtomizerField Optimization Interface v2.1")
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print(f"Device: {self.device}")
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# Load model
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print(f"Loading model from {model_path}...")
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checkpoint = torch.load(model_path, map_location=self.device)
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# Create model
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model_config = checkpoint['config']['model']
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self.model = AtomizerFieldModel(**model_config)
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self.model.load_state_dict(checkpoint['model_state_dict'])
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self.model = self.model.to(self.device)
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self.model.eval()
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self.config = checkpoint['config']
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self.uncertainty_threshold = uncertainty_threshold
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self.enable_gradients = enable_gradients
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# Model info
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self.model_info = {
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'version': checkpoint.get('epoch', 'unknown'),
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'best_val_loss': checkpoint.get('best_val_loss', 'unknown'),
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'training_config': checkpoint['config']
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}
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print(f"Model loaded successfully!")
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print(f" Epoch: {checkpoint.get('epoch', 'N/A')}")
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print(f" Validation loss: {checkpoint.get('best_val_loss', 'N/A')}")
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# Statistics for tracking
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self.eval_count = 0
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self.total_time = 0.0
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def evaluate(self, graph_data, return_fields=False):
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"""
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Evaluate design using neural network (drop-in FEA replacement)
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Args:
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graph_data: PyTorch Geometric Data object with mesh graph
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return_fields (bool): Return complete fields or just objectives
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Returns:
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dict: Optimization objectives and optionally complete fields
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- max_stress: Maximum von Mises stress (MPa)
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- max_displacement: Maximum displacement (mm)
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- mass: Total mass (kg) if available
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- fields: Complete stress/displacement fields (if return_fields=True)
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- inference_time_ms: Prediction time
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- uncertainty: Prediction uncertainty (if ensemble enabled)
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"""
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start_time = time.time()
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# Move to device
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graph_data = graph_data.to(self.device)
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# Predict
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with torch.set_grad_enabled(self.enable_gradients):
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predictions = self.model(graph_data, return_stress=True)
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inference_time = (time.time() - start_time) * 1000 # ms
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# Extract objectives
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max_displacement = torch.max(
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torch.norm(predictions['displacement'][:, :3], dim=1)
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).item()
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max_stress = torch.max(predictions['von_mises']).item()
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results = {
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'max_stress': max_stress,
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'max_displacement': max_displacement,
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'inference_time_ms': inference_time,
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'evaluation_count': self.eval_count
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}
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# Add complete fields if requested
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if return_fields:
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results['fields'] = {
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'displacement': predictions['displacement'].cpu().detach().numpy(),
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'stress': predictions['stress'].cpu().detach().numpy(),
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'von_mises': predictions['von_mises'].cpu().detach().numpy()
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}
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# Update statistics
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self.eval_count += 1
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self.total_time += inference_time
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return results
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def get_sensitivities(self, graph_data, objective='max_stress'):
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"""
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Compute gradients for gradient-based optimization
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This enables MUCH faster optimization than finite differences!
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Args:
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graph_data: PyTorch Geometric Data with requires_grad=True
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objective (str): Which objective to differentiate ('max_stress' or 'max_displacement')
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Returns:
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dict: Gradients with respect to input features
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- node_gradients: ∂objective/∂node_features
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- edge_gradients: ∂objective/∂edge_features
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"""
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if not self.enable_gradients:
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raise RuntimeError("Gradients not enabled. Set enable_gradients=True")
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# Enable gradients
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graph_data = graph_data.to(self.device)
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graph_data.x.requires_grad_(True)
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if graph_data.edge_attr is not None:
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graph_data.edge_attr.requires_grad_(True)
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# Forward pass
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predictions = self.model(graph_data, return_stress=True)
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# Compute objective
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if objective == 'max_stress':
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obj = torch.max(predictions['von_mises'])
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elif objective == 'max_displacement':
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disp_mag = torch.norm(predictions['displacement'][:, :3], dim=1)
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obj = torch.max(disp_mag)
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else:
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raise ValueError(f"Unknown objective: {objective}")
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# Backward pass
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obj.backward()
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# Extract gradients
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gradients = {
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'node_gradients': graph_data.x.grad.cpu().numpy(),
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'objective_value': obj.item()
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}
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if graph_data.edge_attr is not None and graph_data.edge_attr.grad is not None:
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gradients['edge_gradients'] = graph_data.edge_attr.grad.cpu().numpy()
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return gradients
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def batch_evaluate(self, graph_data_list, return_fields=False):
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"""
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Evaluate multiple designs in batch (even faster!)
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Args:
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graph_data_list (list): List of graph data objects
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return_fields (bool): Return complete fields
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Returns:
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list: List of evaluation results
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"""
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results = []
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for graph_data in graph_data_list:
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result = self.evaluate(graph_data, return_fields=return_fields)
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results.append(result)
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return results
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def needs_fea_validation(self, uncertainty):
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"""
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Determine if FEA validation is recommended
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Args:
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uncertainty (float): Prediction uncertainty
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Returns:
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bool: True if FEA is recommended
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"""
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return uncertainty > self.uncertainty_threshold
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def compare_with_fea(self, graph_data, fea_results):
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"""
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Compare neural predictions with FEA ground truth
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Args:
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graph_data: Mesh graph
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fea_results (dict): FEA results with 'max_stress', 'max_displacement'
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Returns:
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dict: Comparison metrics
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"""
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# Neural prediction
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pred = self.evaluate(graph_data)
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# Compute errors
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stress_error = abs(pred['max_stress'] - fea_results['max_stress'])
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stress_rel_error = stress_error / (fea_results['max_stress'] + 1e-8)
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disp_error = abs(pred['max_displacement'] - fea_results['max_displacement'])
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disp_rel_error = disp_error / (fea_results['max_displacement'] + 1e-8)
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comparison = {
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'neural_prediction': pred,
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'fea_results': fea_results,
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'errors': {
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'stress_error_abs': stress_error,
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'stress_error_rel': stress_rel_error,
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'displacement_error_abs': disp_error,
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'displacement_error_rel': disp_rel_error
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},
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'within_tolerance': stress_rel_error < 0.1 and disp_rel_error < 0.1
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}
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return comparison
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def get_statistics(self):
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"""
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Get optimizer usage statistics
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Returns:
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dict: Statistics about predictions
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"""
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avg_time = self.total_time / self.eval_count if self.eval_count > 0 else 0
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return {
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'total_evaluations': self.eval_count,
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'total_time_ms': self.total_time,
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'average_time_ms': avg_time,
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'model_info': self.model_info
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}
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def reset_statistics(self):
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"""Reset usage statistics"""
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self.eval_count = 0
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self.total_time = 0.0
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class ParametricOptimizer:
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"""
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Optimizer for parametric designs
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This wraps NeuralFieldOptimizer and adds parameter → mesh conversion.
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Enables direct optimization over design parameters (thickness, radius, etc.)
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"""
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def __init__(
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self,
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model_path,
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parameter_names,
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parameter_bounds,
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mesh_generator_fn
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):
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"""
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Initialize parametric optimizer
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Args:
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model_path (str): Path to trained model
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parameter_names (list): Names of design parameters
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parameter_bounds (dict): Bounds for each parameter
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mesh_generator_fn: Function that converts parameters → graph_data
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"""
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self.neural_optimizer = NeuralFieldOptimizer(model_path)
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self.parameter_names = parameter_names
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self.parameter_bounds = parameter_bounds
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self.mesh_generator = mesh_generator_fn
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print(f"\nParametric Optimizer initialized")
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print(f"Design parameters: {parameter_names}")
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def evaluate_parameters(self, parameters):
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"""
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Evaluate design from parameters
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Args:
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parameters (dict): Design parameters
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Returns:
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dict: Objectives (max_stress, max_displacement, etc.)
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"""
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# Generate mesh from parameters
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graph_data = self.mesh_generator(parameters)
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# Evaluate
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results = self.neural_optimizer.evaluate(graph_data)
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# Add parameters to results
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results['parameters'] = parameters
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return results
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def optimize(
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self,
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initial_parameters,
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objectives,
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constraints,
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method='gradient',
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max_iterations=100
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):
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"""
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Run optimization
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Args:
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initial_parameters (dict): Starting point
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objectives (list): Objectives to minimize/maximize
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constraints (list): Constraint functions
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method (str): Optimization method ('gradient' or 'genetic')
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max_iterations (int): Maximum iterations
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Returns:
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dict: Optimal parameters and results
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"""
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# This would integrate with scipy.optimize or genetic algorithms
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# Placeholder for now
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print(f"\nStarting optimization with {method} method...")
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print(f"Initial parameters: {initial_parameters}")
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print(f"Objectives: {objectives}")
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print(f"Max iterations: {max_iterations}")
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# TODO: Implement optimization loop
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# For gradient-based:
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# 1. Evaluate at current parameters
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# 2. Compute sensitivities
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# 3. Update parameters using gradients
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# 4. Repeat until convergence
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raise NotImplementedError("Full optimization loop coming in next update!")
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def create_optimizer(model_path, config=None):
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"""
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Factory function to create optimizer
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Args:
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model_path (str): Path to trained model
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config (dict): Optimizer configuration
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Returns:
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NeuralFieldOptimizer instance
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"""
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if config is None:
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config = {}
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return NeuralFieldOptimizer(model_path, **config)
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if __name__ == "__main__":
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# Example usage
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print("AtomizerField Optimization Interface")
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print("=" * 60)
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print("\nThis module provides fast optimization with neural field predictions.")
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print("\nExample usage:")
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print("""
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# Create optimizer
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optimizer = NeuralFieldOptimizer('checkpoint_best.pt')
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# Evaluate design
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results = optimizer.evaluate(graph_data)
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print(f"Max stress: {results['max_stress']:.2f} MPa")
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print(f"Inference time: {results['inference_time_ms']:.1f} ms")
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# Get sensitivities for gradient-based optimization
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gradients = optimizer.get_sensitivities(graph_data, objective='max_stress')
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# Batch evaluation (test 1000 designs in seconds!)
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all_results = optimizer.batch_evaluate(design_variants)
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""")
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print("\nOptimization interface ready!")
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