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>

atomizer-field/visualization_report.md

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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
+
+![Mesh Structure](visualization_images/mesh.png)
+
+The model contains 5,179 nodes and 4,866 elements.
+
+## Displacement Results
+
+![Displacement Field](visualization_images/displacement.png)
+
+**Maximum Displacement:** 19.556875 mm
+
+The plots show the original mesh (left) and deformed mesh (right) with displacement magnitude shown in color.
+
+## Stress Results
+
+![Stress Field](visualization_images/stress.png)
+
+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*