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>
47 lines
923 B
Markdown
47 lines
923 B
Markdown
# FEA Visualization Report
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**Generated:** 2025-11-24T09:24:10.133023
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**Case:** test_case_beam
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---
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## Model Information
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- **Analysis Type:** SOL_Unknown
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- **Nodes:** 5,179
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- **Elements:** 4,866
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- **Materials:** 1
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## Mesh Structure
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The model contains 5,179 nodes and 4,866 elements.
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## Displacement Results
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**Maximum Displacement:** 19.556875 mm
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The plots show the original mesh (left) and deformed mesh (right) with displacement magnitude shown in color.
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## Stress Results
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The stress distribution is shown with colors representing von Mises stress levels.
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## Summary Statistics
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| Property | Value |
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|----------|-------|
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| Nodes | 5,179 |
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| Elements | 4,866 |
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| Max Displacement | 19.556875 mm |
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---
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*Report generated by AtomizerField Visualizer*
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