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/scripts/create_circular_plate_study.py

@@ -0,0 +1,70 @@
+"""
+Create circular plate frequency tuning study with COMPLETE automation.
+
+This demonstrates the proper Hybrid Mode workflow:
+1. Study structure creation
+2. Benchmarking
+3. Validation
+4. Auto-generated runner
+"""
+
+from pathlib import Path
+import sys
+sys.path.insert(0, str(Path(__file__).parent))
+
+from optimization_engine.hybrid_study_creator import HybridStudyCreator
+
+
+def main():
+    creator = HybridStudyCreator()
+
+    # Create workflow JSON first (in temp location)
+    import json
+    import tempfile
+    workflow = {
+        "study_name": "circular_plate_frequency_tuning",
+        "optimization_request": "Tune the first natural frequency mode to exactly 115 Hz (within 0.1 Hz tolerance)",
+        "design_variables": [
+            {"parameter": "inner_diameter", "bounds": [50, 150]},
+            {"parameter": "plate_thickness", "bounds": [2, 10]}
+        ],
+        "objectives": [{
+            "name": "frequency_error",
+            "goal": "minimize",
+            "extraction": {
+                "action": "extract_first_natural_frequency",
+                "params": {"mode_number": 1, "target_frequency": 115.0}
+            }
+        }],
+        "constraints": [{
+            "name": "frequency_tolerance",
+            "type": "less_than",
+            "threshold": 0.1
+        }]
+    }
+
+    # Write to temp file
+    temp_workflow = Path(tempfile.gettempdir()) / "circular_plate_workflow.json"
+    with open(temp_workflow, 'w') as f:
+        json.dump(workflow, f, indent=2)
+
+    # Create study with complete automation
+    study_dir = creator.create_from_workflow(
+        workflow_json_path=temp_workflow,
+        model_files={
+            'prt': Path("examples/Models/Circular Plate/Circular_Plate.prt"),
+            'sim': Path("examples/Models/Circular Plate/Circular_Plate_sim1.sim"),
+            'fem': Path("examples/Models/Circular Plate/Circular_Plate_fem1.fem"),
+            'fem_i': Path("examples/Models/Circular Plate/Circular_Plate_fem1_i.prt")
+        },
+        study_name="circular_plate_frequency_tuning"
+    )
+
+    print(f"Study ready at: {study_dir}")
+    print()
+    print("Next step:")
+    print(f"  python {study_dir}/run_optimization.py")
+
+
+if __name__ == "__main__":
+    main()