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>
44 lines
1.0 KiB
Plaintext
44 lines
1.0 KiB
Plaintext
# AtomizerField Requirements
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# Python 3.8+ required
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# ============================================================================
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# Phase 1: Data Parser
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# ============================================================================
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# Core FEA parsing
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pyNastran>=1.4.0
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# Numerical computing
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numpy>=1.20.0
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# HDF5 file format for efficient field data storage
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h5py>=3.0.0
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# ============================================================================
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# Phase 2: Neural Network Training
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# ============================================================================
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# Deep learning framework
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torch>=2.0.0
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# Graph neural networks
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torch-geometric>=2.3.0
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# TensorBoard for training visualization
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tensorboard>=2.13.0
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# ============================================================================
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# Optional: Development and Testing
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# ============================================================================
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# Testing
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# pytest>=7.0.0
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# pytest-cov>=4.0.0
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# Visualization
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# matplotlib>=3.5.0
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# plotly>=5.0.0
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# Progress bars
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# tqdm>=4.65.0
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