feat: Add AtomizerField training data export and intelligent model discovery

Major additions:
- Training data export system for AtomizerField neural network training
- Bracket stiffness optimization study with 50+ training samples
- Intelligent NX model discovery (auto-detect solutions, expressions, mesh)
- Result extractors module for displacement, stress, frequency, mass
- User-generated NX journals for advanced workflows
- Archive structure for legacy scripts and test outputs
- Protocol documentation and dashboard launcher

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

archive/test_scripts/test_parametric_surrogate.py

@@ -0,0 +1,61 @@
+"""Test parametric surrogate integration."""
+
+import time
+from optimization_engine.neural_surrogate import create_parametric_surrogate_for_study
+
+print("Testing Parametric Neural Surrogate")
+print("=" * 60)
+
+# Create surrogate with auto-detection
+surrogate = create_parametric_surrogate_for_study()
+
+if surrogate is None:
+    print("ERROR: Failed to create surrogate")
+    exit(1)
+
+print(f"Surrogate created successfully!")
+print(f"  Device: {surrogate.device}")
+print(f"  Nodes: {surrogate.num_nodes}")
+print(f"  Model val_loss: {surrogate.best_val_loss:.4f}")
+print(f"  Design vars: {surrogate.design_var_names}")
+
+# Test prediction with example params
+test_params = {
+    "beam_half_core_thickness": 7.0,
+    "beam_face_thickness": 2.5,
+    "holes_diameter": 35.0,
+    "hole_count": 10.0
+}
+
+print(f"\nTest prediction with params: {test_params}")
+results = surrogate.predict(test_params)
+
+print(f"\nResults:")
+print(f"  Mass: {results['mass']:.2f} g")
+print(f"  Frequency: {results['frequency']:.2f} Hz")
+print(f"  Max displacement: {results['max_displacement']:.6f} mm")
+print(f"  Max stress: {results['max_stress']:.2f} MPa")
+print(f"  Inference time: {results['inference_time_ms']:.2f} ms")
+
+# Speed test
+n = 100
+start = time.time()
+for _ in range(n):
+    surrogate.predict(test_params)
+elapsed = time.time() - start
+
+print(f"\nSpeed test: {n} predictions in {elapsed:.3f}s")
+print(f"  Average: {elapsed/n*1000:.2f} ms per prediction")
+
+# Compare with training data range
+print(f"\nExpected range (from training data):")
+print(f"  Mass: ~2808 - 5107 g")
+print(f"  Frequency: ~15.8 - 21.9 Hz")
+print(f"  Max displacement: ~0.02-0.03 mm")
+
+stats = surrogate.get_statistics()
+print(f"\nStatistics:")
+print(f"  Total predictions: {stats['total_predictions']}")
+print(f"  Average time: {stats['average_time_ms']:.2f} ms")
+
+print("\nParametric surrogate test PASSED!")