feat: Add Protocol 13 adaptive optimization, Plotly charts, and dashboard improvements

## Protocol 13: Adaptive Multi-Objective Optimization
- Iterative FEA + Neural Network surrogate workflow
- Initial FEA sampling, NN training, NN-accelerated search
- FEA validation of top NN predictions, retraining loop
- adaptive_state.json tracks iteration history and best values
- M1 mirror study (V11) with 103 FEA, 3000 NN trials

## Dashboard Visualization Enhancements
- Added Plotly.js interactive charts (parallel coords, Pareto, convergence)
- Lazy loading with React.lazy() for performance
- Code splitting: plotly.js-basic-dist (~1MB vs 3.5MB)
- Chart library toggle (Recharts default, Plotly on-demand)
- ExpandableChart component for full-screen modal views
- ConsoleOutput component for real-time log viewing

## Documentation
- Protocol 13 detailed documentation
- Dashboard visualization guide
- Plotly components README
- Updated run-optimization skill with Mode 5 (adaptive)

## Bug Fixes
- Fixed TypeScript errors in dashboard components
- Fixed Card component to accept ReactNode title
- Removed unused imports across components

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

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

.claude/skills/run-optimization.md

@@ -1,7 +1,7 @@
 # Run Optimization Skill
 
-**Last Updated**: November 25, 2025
-**Version**: 1.0 - Optimization Execution and Monitoring
+**Last Updated**: December 3, 2025
+**Version**: 2.0 - Added Adaptive Multi-Objective (Protocol 13)
 
 You are helping the user run and monitor Atomizer optimization studies.
 
@@ -97,6 +97,42 @@ python run_optimization.py --trials 30 --resume
 ```
 **Use when**: Optimization was interrupted
 
+### Mode 5: Adaptive Multi-Objective (Protocol 13)
+```bash
+cd studies/{study_name}
+python run_optimization.py --start
+```
+**Use when**:
+- FEA takes > 5 minutes per run
+- Multi-objective optimization (2-4 objectives)
+- Need to explore > 100 designs efficiently
+
+**Workflow:**
+1. Initial FEA trials (50-100) for NN training data
+2. Train neural network surrogate
+3. NN-accelerated search (1000+ trials in seconds)
+4. Validate top NN predictions with FEA
+5. Retrain NN with new data, repeat
+
+**Configuration** (in optimization_config.json):
+```json
+{
+  "protocol": 13,
+  "adaptive_settings": {
+    "enabled": true,
+    "initial_fea_trials": 50,
+    "nn_trials_per_iteration": 1000,
+    "fea_validation_per_iteration": 5,
+    "max_iterations": 10
+  }
+}
+```
+
+**Monitoring:**
+- `adaptive_state.json`: Current iteration, best values, history
+- Dashboard shows FEA (blue) vs NN (orange) trials
+- Pareto front updates after each FEA validation
+
 ## Execution Steps
 
 ### Step 1: Validate Study
@@ -254,6 +290,12 @@ MONITORING OPTIONS
    cd atomizer-dashboard/frontend && npm run dev
    → http://localhost:3003
 
+   Features:
+   - ALL extracted metrics displayed per trial (not just mass/frequency)
+   - Quick preview shows first 6 metrics with abbreviations
+   - Expanded view shows full metric names and values
+   - Multi-objective studies: Zernike RMS, coefficients, workload metrics
+
 2. Optuna Dashboard:
    python -c "import optuna; from optuna_dashboard import run_server; ..."
    → http://localhost:8081
@@ -263,6 +305,10 @@ MONITORING OPTIONS
    Tail the log file
 ```
 
+**IMPORTANT**: For multi-objective studies with custom metrics (Zernike, thermal, etc.),
+the Atomizer Dashboard automatically displays ALL numeric metrics from user_attrs.
+No configuration needed - metrics are discovered dynamically.
+
 ## Resource Management
 
 ### Before Running