refactor: Major reorganization of optimization_engine module structure

BREAKING CHANGE: Module paths have been reorganized for better maintainability.
Backwards compatibility aliases with deprecation warnings are provided.

New Structure:
- core/           - Optimization runners (runner, intelligent_optimizer, etc.)
- processors/     - Data processing
  - surrogates/   - Neural network surrogates
- nx/             - NX/Nastran integration (solver, updater, session_manager)
- study/          - Study management (creator, wizard, state, reset)
- reporting/      - Reports and analysis (visualizer, report_generator)
- config/         - Configuration management (manager, builder)
- utils/          - Utilities (logger, auto_doc, etc.)
- future/         - Research/experimental code

Migration:
- ~200 import changes across 125 files
- All __init__.py files use lazy loading to avoid circular imports
- Backwards compatibility layer supports old import paths with warnings
- All existing functionality preserved

To migrate existing code:
  OLD: from optimization_engine.nx_solver import NXSolver
  NEW: from optimization_engine.nx.solver import NXSolver

  OLD: from optimization_engine.runner import OptimizationRunner
  NEW: from optimization_engine.core.runner import OptimizationRunner

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

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

archive/scripts/create_intelligent_study.py

@@ -228,11 +228,11 @@ from pathlib import Path
 # Add optimization engine to path
 sys.path.insert(0, str(Path(__file__).parent.parent.parent))
 
-from optimization_engine.intelligent_optimizer import IntelligentOptimizer
-from optimization_engine.nx_updater import NXParameterUpdater
-from optimization_engine.nx_solver import NXSolver
+from optimization_engine.core.intelligent_optimizer import IntelligentOptimizer
+from optimization_engine.nx.updater import NXParameterUpdater
+from optimization_engine.nx.solver import NXSolver
 from optimization_engine.extractors.frequency_extractor import extract_first_frequency
-from optimization_engine.generate_report_markdown import generate_markdown_report
+from optimization_engine.reporting.markdown_report import generate_markdown_report
 
 
 def main():

archive/scripts/run_calibration_loop.py

@@ -29,7 +29,7 @@ import matplotlib.pyplot as plt
 project_root = Path(__file__).parent
 sys.path.insert(0, str(project_root))
 
-from optimization_engine.active_learning_surrogate import (
+from optimization_engine.processors.surrogates.active_learning_surrogate import (
     ActiveLearningSurrogate,
     extract_training_data_from_study
 )

archive/scripts/run_nn_optimization.py

@@ -21,7 +21,7 @@ project_root = Path(__file__).parent
 sys.path.insert(0, str(project_root))
 sys.path.insert(0, str(project_root / 'atomizer-field'))
 
-from optimization_engine.simple_mlp_surrogate import SimpleSurrogate
+from optimization_engine.processors.surrogates.simple_mlp_surrogate import SimpleSurrogate
 
 
 def main():

archive/scripts/run_validated_nn_optimization.py

@@ -63,7 +63,7 @@ def load_config_bounds(study_path: Path) -> dict:
 
     return bounds
 
-from optimization_engine.active_learning_surrogate import EnsembleMLP
+from optimization_engine.processors.surrogates.active_learning_surrogate import EnsembleMLP
 
 
 class ValidatedSurrogate:

archive/scripts/validate_surrogate_real_data.py

@@ -22,7 +22,7 @@ import matplotlib.pyplot as plt
 project_root = Path(__file__).parent
 sys.path.insert(0, str(project_root))
 
-from optimization_engine.active_learning_surrogate import (
+from optimization_engine.processors.surrogates.active_learning_surrogate import (
     EnsembleMLP,
     extract_training_data_from_study
 )

archive/scripts/visualize_nn_optimization.py

@@ -20,7 +20,7 @@ import optuna
 project_root = Path(__file__).parent
 sys.path.insert(0, str(project_root))
 
-from optimization_engine.simple_mlp_surrogate import SimpleSurrogate
+from optimization_engine.processors.surrogates.simple_mlp_surrogate import SimpleSurrogate
 
 def load_fea_data_from_database(db_path: str, study_name: str):
     """Load actual FEA results from database for comparison."""