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>

atomizer-field/train_parametric.py

@@ -314,17 +314,33 @@ def create_reference_graph(num_nodes: int = 500, device: torch.device = None):
     # Create simple node features (placeholder)
     x = torch.randn(num_nodes, 12, device=device)
 
-    # Create grid-like connectivity
+    # Create grid-like connectivity - ensure valid indices
     edges = []
-    grid_size = int(np.sqrt(num_nodes))
+    grid_size = int(np.ceil(np.sqrt(num_nodes)))
+
     for i in range(num_nodes):
-        # Connect to neighbors
-        if i % grid_size < grid_size - 1:  # Right neighbor
-            edges.append([i, i + 1])
-            edges.append([i + 1, i])
-        if i + grid_size < num_nodes:  # Bottom neighbor
-            edges.append([i, i + grid_size])
-            edges.append([i + grid_size, i])
+        row = i // grid_size
+        col = i % grid_size
+
+        # Right neighbor (same row)
+        right = i + 1
+        if col < grid_size - 1 and right < num_nodes:
+            edges.append([i, right])
+            edges.append([right, i])
+
+        # Bottom neighbor (next row)
+        bottom = i + grid_size
+        if bottom < num_nodes:
+            edges.append([i, bottom])
+            edges.append([bottom, i])
+
+    # Ensure we have at least some edges
+    if len(edges) == 0:
+        # Fallback: fully connected for very small graphs
+        for i in range(num_nodes):
+            for j in range(i + 1, min(i + 5, num_nodes)):
+                edges.append([i, j])
+                edges.append([j, i])
 
     edge_index = torch.tensor(edges, dtype=torch.long, device=device).t().contiguous()
     edge_attr = torch.randn(edge_index.shape[1], 5, device=device)