Anto01
eabcc4c3ca
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>
43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
#!/usr/bin/env python
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"""
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{STUDY_NAME} - Neural Network Acceleration Script (Simplified)
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================================================================
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This script uses ConfigDrivenSurrogate for config-driven NN optimization.
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The ~600 lines of boilerplate code is now handled automatically.
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Workflow:
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---------
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1. First run FEA: python run_optimization.py --run --trials 50
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2. Then run NN: python run_nn_optimization.py --turbo --nn-trials 5000
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Or combine:
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python run_nn_optimization.py --all
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Generated by Atomizer StudyWizard
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"""
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from pathlib import Path
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import sys
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# Add project root to path
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project_root = Path(__file__).resolve().parents[2]
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sys.path.insert(0, str(project_root))
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from optimization_engine.processors.surrogates.generic_surrogate import ConfigDrivenSurrogate
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def main():
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"""Run neural acceleration using config-driven surrogate."""
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# Create surrogate - all config read from optimization_config.json
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surrogate = ConfigDrivenSurrogate(__file__)
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# Element type: 'auto' detects from DAT file
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# Override if needed: surrogate.element_type = 'cquad4' (shell) or 'ctetra' (solid)
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return surrogate.run()
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if __name__ == "__main__":
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exit(main())
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