feat: Major update with validators, skills, dashboard, and docs reorganization

- Add validation framework (config, model, results, study validators)
- Add Claude Code skills (create-study, run-optimization, generate-report,
  troubleshoot, analyze-model)
- Add Atomizer Dashboard (React frontend + FastAPI backend)
- Reorganize docs into structured directories (00-09)
- Add neural surrogate modules and training infrastructure
- Add multi-objective optimization support

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

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

examples/optimization_config_neural.json

@@ -0,0 +1,159 @@
+{
+  "study_name": "beam_optimization_with_neural",
+  "sim_file": "examples/Models/Beam/Beam.sim",
+  "fem_file": "examples/Models/Beam/Beam_fem1.fem",
+
+  "design_variables": [
+    {
+      "name": "width",
+      "expression_name": "width",
+      "min": 20.0,
+      "max": 80.0
+    },
+    {
+      "name": "height",
+      "expression_name": "height",
+      "min": 30.0,
+      "max": 100.0
+    },
+    {
+      "name": "thickness",
+      "expression_name": "thickness",
+      "min": 2.0,
+      "max": 10.0
+    }
+  ],
+
+  "objectives": [
+    {
+      "name": "max_stress",
+      "type": "minimize",
+      "weight": 1.0,
+      "extractor": {
+        "type": "result_parameter",
+        "parameter_name": "Max Von Mises Stress"
+      }
+    },
+    {
+      "name": "mass",
+      "type": "minimize",
+      "weight": 0.5,
+      "extractor": {
+        "type": "expression",
+        "expression_name": "mass"
+      }
+    }
+  ],
+
+  "constraints": [
+    {
+      "name": "stress_limit",
+      "type": "less_than",
+      "value": 250.0,
+      "extractor": {
+        "type": "result_parameter",
+        "parameter_name": "Max Von Mises Stress"
+      }
+    },
+    {
+      "name": "max_displacement",
+      "type": "less_than",
+      "value": 5.0,
+      "extractor": {
+        "type": "result_parameter",
+        "parameter_name": "Max Displacement"
+      }
+    }
+  ],
+
+  "optimization": {
+    "algorithm": "NSGA-II",
+    "n_trials": 200,
+    "population_size": 20
+  },
+
+  "neural_surrogate": {
+    "enabled": true,
+    "model_checkpoint": "atomizer-field/checkpoints/beam_model_v1.0/best_model.pt",
+    "confidence_threshold": 0.85,
+    "fallback_to_fea": true,
+    "ensemble_models": [
+      "atomizer-field/checkpoints/beam_model_v1.0/model_1.pt",
+      "atomizer-field/checkpoints/beam_model_v1.0/model_2.pt",
+      "atomizer-field/checkpoints/beam_model_v1.0/model_3.pt"
+    ],
+    "device": "cuda",
+    "batch_size": 32,
+    "cache_predictions": true
+  },
+
+  "hybrid_optimization": {
+    "enabled": true,
+    "exploration_trials": 20,
+    "training_interval": 50,
+    "validation_frequency": 10,
+    "min_training_samples": 30,
+    "retrain_on_drift": true,
+    "drift_threshold": 0.15,
+    "adaptive_switching": true,
+    "phases": [
+      {
+        "name": "exploration",
+        "trials": [0, 20],
+        "use_nn": false,
+        "description": "Initial FEA-based exploration"
+      },
+      {
+        "name": "training",
+        "trials": [21, 30],
+        "use_nn": false,
+        "description": "Collect training data"
+      },
+      {
+        "name": "exploitation",
+        "trials": [31, 180],
+        "use_nn": true,
+        "description": "Neural network exploitation"
+      },
+      {
+        "name": "validation",
+        "trials": [181, 200],
+        "use_nn": false,
+        "description": "Final FEA validation"
+      }
+    ]
+  },
+
+  "training_data_export": {
+    "enabled": true,
+    "export_dir": "atomizer_field_training_data/beam_study",
+    "export_frequency": 1,
+    "include_failed_trials": false,
+    "compression": "gzip"
+  },
+
+  "neural_training": {
+    "auto_train": true,
+    "training_script": "atomizer-field/train.py",
+    "epochs": 200,
+    "learning_rate": 0.001,
+    "batch_size": 16,
+    "validation_split": 0.2,
+    "early_stopping_patience": 20,
+    "model_architecture": "GraphUNet",
+    "hidden_channels": 128,
+    "num_layers": 4,
+    "dropout": 0.1,
+    "physics_loss_weight": 0.3
+  },
+
+  "performance_tracking": {
+    "track_speedup": true,
+    "track_accuracy": true,
+    "export_metrics": true,
+    "metrics_file": "neural_performance_metrics.json"
+  },
+
+  "version": "2.0",
+  "description": "Beam optimization with AtomizerField neural surrogate for 600x speedup"
+}