602560c46a
Neural Acceleration (MLP Surrogate): - Add run_nn_optimization.py with hybrid FEA/NN workflow - MLP architecture: 4-layer (64->128->128->64) with BatchNorm/Dropout - Three workflow modes: - --all: Sequential export->train->optimize->validate - --hybrid-loop: Iterative Train->NN->Validate->Retrain cycle - --turbo: Aggressive single-best validation (RECOMMENDED) - Turbo mode: 5000 NN trials + 50 FEA validations in ~12 minutes - Separate nn_study.db to avoid overloading dashboard Performance Results (bracket_pareto_3obj study): - NN prediction errors: mass 1-5%, stress 1-4%, stiffness 5-15% - Found minimum mass designs at boundary (angle~30deg, thick~30mm) - 100x speedup vs pure FEA exploration Protocol Operating System: - Add .claude/skills/ with Bootstrap, Cheatsheet, Context Loader - Add docs/protocols/ with operations (OP_01-06) and system (SYS_10-14) - Update SYS_14_NEURAL_ACCELERATION.md with MLP Turbo Mode docs NX Automation: - Add optimization_engine/hooks/ for NX CAD/CAE automation - Add study_wizard.py for guided study creation - Fix FEM mesh update: load idealized part before UpdateFemodel() New Study: - bracket_pareto_3obj: 3-objective Pareto (mass, stress, stiffness) - 167 FEA trials + 5000 NN trials completed - Demonstrates full hybrid workflow 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
49 lines
1.1 KiB
Python
49 lines
1.1 KiB
Python
"""
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Reset study - Delete results database and logs.
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Usage:
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python reset_study.py
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python reset_study.py --confirm # Skip confirmation
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"""
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from pathlib import Path
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import shutil
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def main():
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument('--confirm', action='store_true', help='Skip confirmation')
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args = parser.parse_args()
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study_dir = Path(__file__).parent
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results_dir = study_dir / "2_results"
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if not args.confirm:
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print(f"This will delete all results in: {results_dir}")
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response = input("Are you sure? (y/N): ")
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if response.lower() != 'y':
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print("Cancelled.")
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return
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# Delete database files
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for f in results_dir.glob("*.db"):
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f.unlink()
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print(f"Deleted: {f.name}")
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# Delete log files
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for f in results_dir.glob("*.log"):
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f.unlink()
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print(f"Deleted: {f.name}")
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# Delete JSON results
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for f in results_dir.glob("*.json"):
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f.unlink()
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print(f"Deleted: {f.name}")
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print("Study reset complete.")
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if __name__ == "__main__":
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main()
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