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>
417 lines
15 KiB
Python
417 lines
15 KiB
Python
"""
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data_loader.py
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Data loading pipeline for neural field training
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AtomizerField Data Loader v2.0
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Converts parsed FEA data (HDF5 + JSON) into PyTorch Geometric graphs for training.
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Key Transformation:
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Parsed FEA Data → Graph Representation → Neural Network Input
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Graph structure:
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- Nodes: FEA mesh nodes (with coordinates, BCs, loads)
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- Edges: Element connectivity (with material properties)
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- Labels: Displacement and stress fields (ground truth from FEA)
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"""
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import json
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import h5py
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import numpy as np
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from pathlib import Path
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import torch
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from torch.utils.data import Dataset
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from torch_geometric.data import Data
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from torch_geometric.loader import DataLoader
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import warnings
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class FEAMeshDataset(Dataset):
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"""
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PyTorch Dataset for FEA mesh data
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Loads parsed neural field data and converts to PyTorch Geometric graphs.
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Each graph represents one FEA analysis case.
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"""
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def __init__(
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self,
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case_directories,
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normalize=True,
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include_stress=True,
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cache_in_memory=False
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):
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"""
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Initialize dataset
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Args:
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case_directories (list): List of paths to parsed cases
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normalize (bool): Normalize node coordinates and results
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include_stress (bool): Include stress in targets
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cache_in_memory (bool): Load all data into RAM (faster but memory-intensive)
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"""
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self.case_dirs = [Path(d) for d in case_directories]
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self.normalize = normalize
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self.include_stress = include_stress
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self.cache_in_memory = cache_in_memory
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# Validate all cases exist
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self.valid_cases = []
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for case_dir in self.case_dirs:
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if self._validate_case(case_dir):
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self.valid_cases.append(case_dir)
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else:
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warnings.warn(f"Skipping invalid case: {case_dir}")
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print(f"Loaded {len(self.valid_cases)}/{len(self.case_dirs)} valid cases")
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# Cache data if requested
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self.cache = {}
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if cache_in_memory:
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print("Caching data in memory...")
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for idx in range(len(self.valid_cases)):
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self.cache[idx] = self._load_case(idx)
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print("Cache complete!")
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# Compute normalization statistics
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if normalize:
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self._compute_normalization_stats()
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def _validate_case(self, case_dir):
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"""Check if case has required files"""
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json_file = case_dir / "neural_field_data.json"
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h5_file = case_dir / "neural_field_data.h5"
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return json_file.exists() and h5_file.exists()
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def __len__(self):
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return len(self.valid_cases)
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def __getitem__(self, idx):
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"""
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Get graph data for one case
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Returns:
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torch_geometric.data.Data object with:
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- x: Node features [num_nodes, feature_dim]
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- edge_index: Element connectivity [2, num_edges]
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- edge_attr: Edge features (material props) [num_edges, edge_dim]
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- y_displacement: Target displacement [num_nodes, 6]
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- y_stress: Target stress [num_nodes, 6] (if include_stress)
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- bc_mask: Boundary condition mask [num_nodes, 6]
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- pos: Node positions [num_nodes, 3]
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"""
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if self.cache_in_memory and idx in self.cache:
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return self.cache[idx]
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return self._load_case(idx)
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def _load_case(self, idx):
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"""Load and process a single case"""
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case_dir = self.valid_cases[idx]
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# Load JSON metadata
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with open(case_dir / "neural_field_data.json", 'r') as f:
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metadata = json.load(f)
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# Load HDF5 field data
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with h5py.File(case_dir / "neural_field_data.h5", 'r') as f:
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# Node coordinates
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node_coords = torch.from_numpy(f['mesh/node_coordinates'][:]).float()
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# Displacement field (target)
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displacement = torch.from_numpy(f['results/displacement'][:]).float()
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# Stress field (target, if available)
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stress = None
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if self.include_stress and 'results/stress' in f:
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# Try to load first available stress type
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stress_group = f['results/stress']
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for stress_type in stress_group.keys():
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stress_data = stress_group[stress_type]['data'][:]
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stress = torch.from_numpy(stress_data).float()
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break
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# Build graph structure
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graph_data = self._build_graph(metadata, node_coords, displacement, stress)
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# Normalize if requested
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if self.normalize:
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graph_data = self._normalize_graph(graph_data)
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return graph_data
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def _build_graph(self, metadata, node_coords, displacement, stress):
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"""
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Convert FEA mesh to graph
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Args:
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metadata (dict): Parsed metadata
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node_coords (Tensor): Node positions [num_nodes, 3]
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displacement (Tensor): Displacement field [num_nodes, 6]
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stress (Tensor): Stress field [num_nodes, 6] or None
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Returns:
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torch_geometric.data.Data
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"""
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num_nodes = node_coords.shape[0]
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# === NODE FEATURES ===
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# Start with coordinates
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node_features = [node_coords] # [num_nodes, 3]
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# Add boundary conditions (which DOFs are constrained)
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bc_mask = torch.zeros(num_nodes, 6) # [num_nodes, 6]
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if 'boundary_conditions' in metadata and 'spc' in metadata['boundary_conditions']:
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for spc in metadata['boundary_conditions']['spc']:
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node_id = spc['node']
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# Find node index (assuming node IDs are sequential starting from 1)
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# This is a simplification - production code should use ID mapping
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if node_id <= num_nodes:
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dofs = spc['dofs']
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# Parse DOF string (e.g., "123" means constrained in x,y,z)
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for dof_char in str(dofs):
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if dof_char.isdigit():
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dof_idx = int(dof_char) - 1 # 0-indexed
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if 0 <= dof_idx < 6:
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bc_mask[node_id - 1, dof_idx] = 1.0
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node_features.append(bc_mask) # [num_nodes, 6]
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# Add load information (force magnitude at each node)
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load_features = torch.zeros(num_nodes, 3) # [num_nodes, 3] for x,y,z forces
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if 'loads' in metadata and 'point_forces' in metadata['loads']:
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for force in metadata['loads']['point_forces']:
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node_id = force['node']
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if node_id <= num_nodes:
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magnitude = force['magnitude']
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direction = force['direction']
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force_vector = [magnitude * d for d in direction]
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load_features[node_id - 1] = torch.tensor(force_vector)
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node_features.append(load_features) # [num_nodes, 3]
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# Concatenate all node features
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x = torch.cat(node_features, dim=-1) # [num_nodes, 3+6+3=12]
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# === EDGE FEATURES ===
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# Build edge index from element connectivity
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edge_index = []
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edge_attrs = []
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# Get material properties
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material_dict = {}
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if 'materials' in metadata:
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for mat in metadata['materials']:
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mat_id = mat['id']
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if mat['type'] == 'MAT1':
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material_dict[mat_id] = [
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mat.get('E', 0.0) / 1e6, # Normalize E (MPa → GPa)
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mat.get('nu', 0.0),
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mat.get('rho', 0.0) * 1e6, # Normalize rho
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mat.get('G', 0.0) / 1e6 if mat.get('G') else 0.0,
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mat.get('alpha', 0.0) * 1e6 if mat.get('alpha') else 0.0
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]
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# Process elements to create edges
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if 'mesh' in metadata and 'elements' in metadata['mesh']:
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for elem_type in ['solid', 'shell', 'beam']:
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if elem_type in metadata['mesh']['elements']:
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for elem in metadata['mesh']['elements'][elem_type]:
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elem_nodes = elem['nodes']
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mat_id = elem.get('material_id', 1)
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# Get material properties for this element
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mat_props = material_dict.get(mat_id, [0.0] * 5)
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# Create edges between all node pairs in element
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# (fully connected within element)
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for i in range(len(elem_nodes)):
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for j in range(i + 1, len(elem_nodes)):
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node_i = elem_nodes[i] - 1 # 0-indexed
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node_j = elem_nodes[j] - 1
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if node_i < num_nodes and node_j < num_nodes:
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# Add bidirectional edges
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edge_index.append([node_i, node_j])
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edge_index.append([node_j, node_i])
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# Both edges get same material properties
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edge_attrs.append(mat_props)
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edge_attrs.append(mat_props)
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# Convert to tensors
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if edge_index:
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edge_index = torch.tensor(edge_index, dtype=torch.long).t() # [2, num_edges]
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edge_attr = torch.tensor(edge_attrs, dtype=torch.float) # [num_edges, 5]
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else:
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# No edges (shouldn't happen, but handle gracefully)
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edge_index = torch.zeros((2, 0), dtype=torch.long)
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edge_attr = torch.zeros((0, 5), dtype=torch.float)
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# === CREATE DATA OBJECT ===
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data = Data(
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x=x,
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edge_index=edge_index,
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edge_attr=edge_attr,
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y_displacement=displacement,
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bc_mask=bc_mask,
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pos=node_coords # Store original positions
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)
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# Add stress if available
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if stress is not None:
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data.y_stress = stress
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return data
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def _normalize_graph(self, data):
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"""
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Normalize graph features
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- Coordinates: Center and scale to unit box
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- Displacement: Scale by mean displacement
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- Stress: Scale by mean stress
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"""
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# Normalize coordinates (already done in node features)
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if hasattr(self, 'coord_mean') and hasattr(self, 'coord_std'):
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# Extract coords from features (first 3 dimensions)
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coords = data.x[:, :3]
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coords_norm = (coords - self.coord_mean) / (self.coord_std + 1e-8)
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data.x[:, :3] = coords_norm
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# Normalize displacement
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if hasattr(self, 'disp_mean') and hasattr(self, 'disp_std'):
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data.y_displacement = (data.y_displacement - self.disp_mean) / (self.disp_std + 1e-8)
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# Normalize stress
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if hasattr(data, 'y_stress') and hasattr(self, 'stress_mean') and hasattr(self, 'stress_std'):
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data.y_stress = (data.y_stress - self.stress_mean) / (self.stress_std + 1e-8)
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return data
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def _compute_normalization_stats(self):
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"""
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Compute mean and std for normalization across entire dataset
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"""
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print("Computing normalization statistics...")
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all_coords = []
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all_disp = []
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all_stress = []
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for idx in range(len(self.valid_cases)):
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case_dir = self.valid_cases[idx]
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with h5py.File(case_dir / "neural_field_data.h5", 'r') as f:
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coords = f['mesh/node_coordinates'][:]
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disp = f['results/displacement'][:]
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all_coords.append(coords)
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all_disp.append(disp)
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# Load stress if available
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if self.include_stress and 'results/stress' in f:
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stress_group = f['results/stress']
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for stress_type in stress_group.keys():
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stress_data = stress_group[stress_type]['data'][:]
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all_stress.append(stress_data)
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break
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# Concatenate all data
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all_coords = np.concatenate(all_coords, axis=0)
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all_disp = np.concatenate(all_disp, axis=0)
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# Compute statistics
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self.coord_mean = torch.from_numpy(all_coords.mean(axis=0)).float()
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self.coord_std = torch.from_numpy(all_coords.std(axis=0)).float()
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self.disp_mean = torch.from_numpy(all_disp.mean(axis=0)).float()
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self.disp_std = torch.from_numpy(all_disp.std(axis=0)).float()
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if all_stress:
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all_stress = np.concatenate(all_stress, axis=0)
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self.stress_mean = torch.from_numpy(all_stress.mean(axis=0)).float()
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self.stress_std = torch.from_numpy(all_stress.std(axis=0)).float()
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print("Normalization statistics computed!")
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def create_dataloaders(
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train_cases,
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val_cases,
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batch_size=4,
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num_workers=0,
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normalize=True,
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include_stress=True
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):
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"""
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Create training and validation dataloaders
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Args:
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train_cases (list): List of training case directories
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val_cases (list): List of validation case directories
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batch_size (int): Batch size
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num_workers (int): Number of data loading workers
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normalize (bool): Normalize features
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include_stress (bool): Include stress targets
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Returns:
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train_loader, val_loader
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"""
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print("\nCreating datasets...")
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# Create datasets
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train_dataset = FEAMeshDataset(
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train_cases,
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normalize=normalize,
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include_stress=include_stress,
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cache_in_memory=False # Set to True for small datasets
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)
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val_dataset = FEAMeshDataset(
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val_cases,
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normalize=normalize,
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include_stress=include_stress,
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cache_in_memory=False
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)
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# Share normalization stats with validation set
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if normalize and hasattr(train_dataset, 'coord_mean'):
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val_dataset.coord_mean = train_dataset.coord_mean
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val_dataset.coord_std = train_dataset.coord_std
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val_dataset.disp_mean = train_dataset.disp_mean
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val_dataset.disp_std = train_dataset.disp_std
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if hasattr(train_dataset, 'stress_mean'):
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val_dataset.stress_mean = train_dataset.stress_mean
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val_dataset.stress_std = train_dataset.stress_std
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# Create dataloaders
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train_loader = DataLoader(
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train_dataset,
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batch_size=batch_size,
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shuffle=True,
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num_workers=num_workers
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)
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val_loader = DataLoader(
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val_dataset,
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batch_size=batch_size,
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shuffle=False,
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num_workers=num_workers
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)
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print(f"\nDataloaders created:")
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print(f" Training: {len(train_dataset)} cases")
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print(f" Validation: {len(val_dataset)} cases")
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return train_loader, val_loader
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if __name__ == "__main__":
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# Test data loader
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print("Testing FEA Mesh Data Loader...\n")
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# This is a placeholder test - you would use actual parsed case directories
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print("Note: This test requires actual parsed FEA data.")
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print("Run the parser first on your NX Nastran files.")
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print("\nData loader implementation complete!")
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