feat: Complete Phase 3 - pyNastran Documentation Integration

Phase 3 implements automated OP2 extraction code generation using
pyNastran documentation research. This completes the zero-manual-coding
pipeline for FEA optimization workflows.

Key Features:
- PyNastranResearchAgent for automated OP2 code generation
- Documentation research via WebFetch integration
- 3 core extraction patterns (displacement, stress, force)
- Knowledge base architecture for learned patterns
- Successfully tested on real OP2 files

Phase 2.9 Integration:
- Updated HookGenerator with lifecycle hook generation
- Added POST_CALCULATION hook point to hooks.py
- Created post_calculation/ plugin directory
- Generated hooks integrate seamlessly with HookManager

New Files:
- optimization_engine/pynastran_research_agent.py (600+ lines)
- optimization_engine/hook_generator.py (800+ lines)
- optimization_engine/inline_code_generator.py
- optimization_engine/plugins/post_calculation/
- tests/test_lifecycle_hook_integration.py
- docs/SESSION_SUMMARY_PHASE_3.md
- docs/SESSION_SUMMARY_PHASE_2_9.md
- docs/SESSION_SUMMARY_PHASE_2_8.md
- docs/HOOK_ARCHITECTURE.md

Modified Files:
- README.md - Added Phase 3 completion status
- optimization_engine/plugins/hooks.py - Added POST_CALCULATION hook

Test Results:
- Phase 3 research agent: PASSED
- Real OP2 extraction: PASSED (max_disp=0.362mm)
- Lifecycle hook integration: PASSED

Generated with Claude Code

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

optimization_engine/plugins/post_calculation/weighted_objective_test.py

@@ -0,0 +1,73 @@
+"""
+Combine normalized stress (70%) and displacement (30%)
+Auto-generated lifecycle hook by Atomizer Phase 2.9
+
+Hook Point: post_calculation
+Inputs: norm_stress, norm_disp
+Outputs: weighted_objective
+"""
+
+import logging
+from typing import Dict, Any, Optional
+
+logger = logging.getLogger(__name__)
+
+
+def weighted_objective_hook(context: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+    """
+    Combine normalized stress (70%) and displacement (30%)
+
+    Args:
+        context: Hook context containing:
+            - trial_number: Current optimization trial
+            - results: Dictionary with extracted FEA results
+            - calculations: Dictionary with inline calculation results
+
+    Returns:
+        Dictionary with calculated values to add to context
+    """
+    logger.info(f"Executing weighted_objective_hook for trial {context.get('trial_number', 'unknown')}")
+
+    # Extract inputs from context
+    results = context.get('results', {})
+    calculations = context.get('calculations', {})
+
+    norm_stress = calculations.get('norm_stress') or results.get('norm_stress')
+    if norm_stress is None:
+        logger.error(f"Required input 'norm_stress' not found in context")
+        raise ValueError(f"Missing required input: norm_stress")
+
+    norm_disp = calculations.get('norm_disp') or results.get('norm_disp')
+    if norm_disp is None:
+        logger.error(f"Required input 'norm_disp' not found in context")
+        raise ValueError(f"Missing required input: norm_disp")
+
+    # Calculate weighted objective
+    result = 0.7 * norm_stress + 0.3 * norm_disp
+
+    logger.info(f"Weighted objective calculated: {result:.6f}")
+
+    return {
+        'weighted_objective': result,
+        'weighted_objective': result
+    }
+
+
+def register_hooks(hook_manager):
+    """
+    Register this hook with the HookManager.
+
+    This function is called automatically when the plugin is loaded.
+
+    Args:
+        hook_manager: The HookManager instance
+    """
+    hook_manager.register_hook(
+        hook_point='post_calculation',
+        function=weighted_objective_hook,
+        description="Combine normalized stress (70%) and displacement (30%)",
+        name="weighted_objective_hook",
+        priority=100,
+        enabled=True
+    )
+    logger.info(f"Registered weighted_objective_hook at post_calculation")