Files

Antoine d5ffba099e 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>

2025-11-26 15:31:33 -05:00

923 B

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FEA Visualization Report

Generated: 2025-11-24T09:24:10.133023

Case: test_case_beam

Model Information

Analysis Type: SOL_Unknown
Nodes: 5,179
Elements: 4,866
Materials: 1

Mesh Structure

The model contains 5,179 nodes and 4,866 elements.

Displacement Results

Maximum Displacement: 19.556875 mm

The plots show the original mesh (left) and deformed mesh (right) with displacement magnitude shown in color.

Stress Results

The stress distribution is shown with colors representing von Mises stress levels.

Summary Statistics

Property	Value
Nodes	5,179
Elements	4,866
Max Displacement	19.556875 mm

Report generated by AtomizerField Visualizer

923 B Raw Blame History