8cbdbcad78
## 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>
1035 lines
40 KiB
Python
1035 lines
40 KiB
Python
"""
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Bracket Stiffness Optimization with AtomizerField Neural Acceleration
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======================================================================
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Multi-objective optimization: maximize stiffness, minimize mass
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Protocol: NSGA-II (Protocol 11)
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This script demonstrates the complete AtomizerField workflow:
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1. FEA Exploration Phase (50 trials) - Collects training data
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2. Auto-Training - Triggers neural network training at 50 points
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3. Neural Acceleration Phase (50+ trials) - 2,200x faster optimization
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Staged Workflow:
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----------------
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1. VALIDATE: Clean old solver files, run 1 trial to validate setup
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python run_optimization.py --validate
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2. TEST: Run 3 trials as integration test
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python run_optimization.py --test
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3. RUN: Launch official optimization
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python run_optimization.py --run --trials 100
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Other Usage:
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# Resume existing study
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python run_optimization.py --run --trials 25 --resume
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# Enable neural acceleration (requires trained model)
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python run_optimization.py --run --trials 100 --enable-nn --resume
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"""
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from pathlib import Path
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import sys
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import json
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import argparse
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from datetime import datetime
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from typing import Optional, Tuple, List
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# Add parent directory 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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import optuna
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from optuna.samplers import NSGAIISampler
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# Use NXSolver (subprocess-based) instead of direct NXOpen imports
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from optimization_engine.nx_solver import NXSolver
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# Import extractors
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from optimization_engine.extractors.bdf_mass_extractor import extract_mass_from_bdf
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from optimization_engine.extractors.extract_displacement import extract_displacement
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# Import structured logger
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from optimization_engine.logger import get_logger
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# Import training data exporter for AtomizerField
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from optimization_engine.training_data_exporter import TrainingDataExporter
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# Import neural surrogate for fast predictions
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from optimization_engine.neural_surrogate import create_surrogate_for_study, NeuralSurrogate, ParametricSurrogate
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def load_config(config_file: Path) -> dict:
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"""Load configuration from JSON file."""
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with open(config_file, 'r') as f:
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return json.load(f)
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def clean_nastran_files(model_dir: Path, logger) -> List[Path]:
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"""
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Remove old Nastran solver output files to ensure fresh validation.
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Cleans: *.op2, *.f06, *.log, *.f04, *.pch, _temp*.txt
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Preserves: *.dat, *.bdf, *.prt, *.sim, *.fem
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Args:
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model_dir: Path to model directory containing solver files
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logger: Logger instance for reporting
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Returns:
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List of deleted file paths
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"""
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nastran_extensions = ['*.op2', '*.f06', '*.log', '*.f04', '*.pch', '*.DBALL', '*.MASTER']
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temp_patterns = ['_temp*.txt', '*_temp_*']
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deleted_files = []
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logger.info(f"\n{'='*60}")
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logger.info("CLEANING OLD NASTRAN FILES")
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logger.info(f"{'='*60}")
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logger.info(f"Model directory: {model_dir}")
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for pattern in nastran_extensions + temp_patterns:
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matches = list(model_dir.glob(pattern))
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for file_path in matches:
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try:
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file_path.unlink()
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deleted_files.append(file_path)
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logger.info(f" Deleted: {file_path.name}")
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except Exception as e:
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logger.warning(f" Failed to delete {file_path.name}: {e}")
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if deleted_files:
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logger.info(f"\nCleaned {len(deleted_files)} files")
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else:
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logger.info("\nNo old solver files found (directory is clean)")
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return deleted_files
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def run_validation(config: dict, nx_solver, model_dir: Path, results_dir: Path,
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study_name: str, logger) -> bool:
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"""
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Run single trial validation to verify setup.
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This validates:
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- NX connection and journal execution
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- Expression updates work correctly
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- Solver completes successfully
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- Result extraction works
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Returns:
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True if validation passed, False otherwise
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"""
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logger.info(f"\n{'='*60}")
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logger.info("VALIDATION MODE - Single Trial Test")
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logger.info(f"{'='*60}")
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# Create temporary study for validation
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storage = f"sqlite:///{results_dir / 'study_validation.db'}"
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sampler = NSGAIISampler(population_size=5, seed=42)
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study = optuna.create_study(
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study_name=f"{study_name}_validation",
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storage=storage,
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sampler=sampler,
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directions=['minimize', 'minimize'],
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load_if_exists=False
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)
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# Run single trial
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logger.info("\nRunning validation trial...")
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try:
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study.optimize(
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lambda trial: fea_objective(trial, config, nx_solver, model_dir, logger, None),
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n_trials=1,
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show_progress_bar=False
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)
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# Check result
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if len(study.trials) == 1:
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trial = study.trials[0]
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if trial.state == optuna.trial.TrialState.COMPLETE:
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stiffness = trial.user_attrs.get('stiffness', 'N/A')
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mass = trial.user_attrs.get('mass', 'N/A')
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disp = trial.user_attrs.get('max_displacement', 'N/A')
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logger.info(f"\n{'='*60}")
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logger.info("VALIDATION PASSED!")
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logger.info(f"{'='*60}")
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logger.info(f"Results from validation trial:")
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logger.info(f" Stiffness: {stiffness:.2f} N/mm" if isinstance(stiffness, float) else f" Stiffness: {stiffness}")
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logger.info(f" Mass: {mass:.4f} kg" if isinstance(mass, float) else f" Mass: {mass}")
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logger.info(f" Max Displacement: {disp:.6f} mm" if isinstance(disp, float) else f" Max Displacement: {disp}")
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logger.info(f"\nDesign variables used:")
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for var in config['design_variables']:
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param = var['parameter']
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value = trial.params.get(param, 'N/A')
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logger.info(f" {param}: {value}")
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logger.info(f"\n{'='*60}")
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logger.info("Next steps:")
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logger.info(" 1. Review results above for sanity")
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logger.info(" 2. Run integration test: python run_optimization.py --test")
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logger.info(" 3. Launch full optimization: python run_optimization.py --run --trials 100")
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logger.info(f"{'='*60}")
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return True
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else:
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logger.error(f"\nVALIDATION FAILED!")
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logger.error(f"Trial state: {trial.state}")
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return False
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else:
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logger.error(f"\nVALIDATION FAILED - No trials completed")
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return False
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except Exception as e:
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logger.error(f"\nVALIDATION FAILED with exception: {e}")
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import traceback
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traceback.print_exc()
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return False
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def discover_output_files(model_dir: Path, logger) -> dict:
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"""
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Scan model directory for all Nastran/FEA output files after a solve.
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This helps identify what data is available for extraction.
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Returns:
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dict with categorized files and their details
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"""
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logger.info(f"\n{'='*60}")
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logger.info("DISCOVERING OUTPUT FILES")
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logger.info(f"{'='*60}")
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discovered = {
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'nastran_results': [], # .op2, .f06, .pch
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'nastran_input': [], # .dat, .bdf
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'nx_files': [], # .prt, .sim, .fem
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'temp_files': [], # _temp*.txt
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'mesh_files': [], # .vtk, .vtu, .h5
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'other': []
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}
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patterns = {
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'nastran_results': ['*.op2', '*.f06', '*.pch', '*.f04', '*.log', '*.DBALL', '*.MASTER'],
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'nastran_input': ['*.dat', '*.bdf'],
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'nx_files': ['*.prt', '*.sim', '*.fem'],
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'temp_files': ['_temp*.txt', '*_temp_*'],
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'mesh_files': ['*.vtk', '*.vtu', '*.h5']
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}
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for category, globs in patterns.items():
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for pattern in globs:
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for file_path in model_dir.glob(pattern):
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file_info = {
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'name': file_path.name,
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'path': str(file_path),
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'size_kb': file_path.stat().st_size / 1024,
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'modified': datetime.fromtimestamp(file_path.stat().st_mtime).strftime('%Y-%m-%d %H:%M:%S')
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}
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discovered[category].append(file_info)
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# Report findings
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logger.info(f"\nModel directory: {model_dir}\n")
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for category, files in discovered.items():
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if files:
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logger.info(f"{category.upper().replace('_', ' ')}:")
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for f in files:
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logger.info(f" {f['name']:40} {f['size_kb']:>10.1f} KB ({f['modified']})")
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# Summary
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total_files = sum(len(files) for files in discovered.values())
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logger.info(f"\nTotal files found: {total_files}")
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# Key files for AtomizerField
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op2_files = [f for f in discovered['nastran_results'] if f['name'].endswith('.op2')]
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dat_files = [f for f in discovered['nastran_input'] if f['name'].endswith('.dat')]
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if op2_files:
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logger.info(f"\n[OK] OP2 file found: {op2_files[0]['name']} - Field data available for AtomizerField")
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else:
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logger.warning("\n[!] No OP2 file found - Run a solve first")
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if dat_files:
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logger.info(f"[OK] DAT file found: {dat_files[0]['name']} - Mass extraction available")
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else:
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logger.warning("[!] No DAT file found - Check simulation setup")
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return discovered
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def run_discovery(config: dict, nx_solver, model_dir: Path, results_dir: Path,
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study_name: str, logger) -> bool:
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"""
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Discovery mode: Intelligently scan model, then run ONE solve to discover all outputs.
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This is the first step when setting up a new study - it shows you what
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data is available for extraction and AtomizerField training.
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Steps:
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1. Scan model to discover solutions, expressions, mesh info
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2. Clean old solver output files
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3. Run ONE FEA solve (using first discovered solution)
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4. Scan and report all generated files
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5. Provide configuration guidance
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Returns:
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True if discovery completed successfully
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"""
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logger.info(f"\n{'='*60}")
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logger.info("INTELLIGENT DISCOVERY MODE")
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logger.info(f"{'='*60}")
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logger.info("This mode will:")
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logger.info(" 1. Scan model for solutions, expressions, and mesh info")
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logger.info(" 2. Clean old solver output files")
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logger.info(" 3. Run ONE FEA solve")
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logger.info(" 4. Scan and report all generated files")
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logger.info(" 5. Provide configuration guidance")
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sim_file = model_dir / config['simulation']['sim_file']
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# =========================================================================
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# STEP 1: INTELLIGENT MODEL SCAN (discover solutions, expressions, etc.)
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# =========================================================================
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logger.info(f"\n{'='*60}")
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logger.info("STEP 1: SCANNING MODEL STRUCTURE")
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logger.info(f"{'='*60}")
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model_info = nx_solver.discover_model(sim_file)
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if model_info.get('success'):
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# Report discovered solutions
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solutions = model_info.get('solutions', [])
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if solutions:
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logger.info(f"\n[OK] Found {len(solutions)} solution(s):")
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for sol in solutions:
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logger.info(f" - {sol['name']} (use this in config: \"solution_name\": \"{sol['name']}\")")
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# Auto-detect solution name for this run
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discovered_solution = solutions[0]['name']
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logger.info(f"\n[AUTO] Will use first solution: \"{discovered_solution}\"")
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else:
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logger.warning("\n[!] No solutions found in model - will solve all")
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discovered_solution = None
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# Report discovered expressions
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expressions = model_info.get('expressions', [])
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if expressions:
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logger.info(f"\n[OK] Found {len(expressions)} expression(s) (potential design variables):")
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for expr in expressions[:10]: # Show first 10
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value_str = f" = {expr.get('value', '?')}" if expr.get('value') else ""
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logger.info(f" - {expr['name']}{value_str}")
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if len(expressions) > 10:
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logger.info(f" ... and {len(expressions) - 10} more")
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else:
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logger.info("\n[INFO] No expressions found (model may use direct parameters)")
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# Report mesh info
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mesh_info = model_info.get('mesh_info', {})
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if mesh_info:
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logger.info(f"\n[OK] Mesh info:")
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logger.info(f" Elements: {mesh_info.get('element_count', 'Unknown')}")
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logger.info(f" Nodes: {mesh_info.get('node_count', 'Unknown')}")
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else:
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logger.warning(f"\n[!] Model scan failed: {model_info.get('error', 'Unknown error')}")
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logger.warning(" Falling back to config-specified solution name")
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discovered_solution = config['simulation'].get('solution_name')
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# =========================================================================
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# STEP 2: CLEAN OLD FILES
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# =========================================================================
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clean_nastran_files(model_dir, logger)
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# =========================================================================
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# STEP 3: RUN SINGLE SOLVE
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# =========================================================================
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logger.info(f"\n{'='*60}")
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logger.info("STEP 3: RUNNING SINGLE SOLVE")
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logger.info(f"{'='*60}")
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# Use discovered solution or fall back to config
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solution_to_use = discovered_solution or config['simulation'].get('solution_name')
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if solution_to_use:
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logger.info(f" Using solution: \"{solution_to_use}\"")
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else:
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logger.info(f" Solving ALL solutions (no specific solution specified)")
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# Create design vars at midpoint of bounds
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design_vars = {}
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for var in config['design_variables']:
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param_name = var['parameter']
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bounds = var['bounds']
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midpoint = (bounds[0] + bounds[1]) / 2
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design_vars[param_name] = midpoint
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logger.info(f" {param_name}: {midpoint:.4f} (midpoint of [{bounds[0]}, {bounds[1]}])")
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try:
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result = nx_solver.run_simulation(
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sim_file=sim_file,
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working_dir=model_dir,
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expression_updates=design_vars,
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solution_name=solution_to_use,
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cleanup=False # Keep all files for discovery
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)
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if not result['success']:
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logger.error(f"\nSolve FAILED: {result.get('error', 'Unknown error')}")
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logger.error("Check that NX is running and model is properly set up")
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return False
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logger.info(f"\nSolve SUCCESSFUL!")
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logger.info(f" OP2 file: {result.get('op2_file', 'N/A')}")
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except Exception as e:
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logger.error(f"\nSolve FAILED with exception: {e}")
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import traceback
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traceback.print_exc()
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return False
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# =========================================================================
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# STEP 4: DISCOVER OUTPUT FILES
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# =========================================================================
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discovered = discover_output_files(model_dir, logger)
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# =========================================================================
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# STEP 5: CONFIGURATION GUIDANCE
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# =========================================================================
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logger.info(f"\n{'='*60}")
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logger.info("CONFIGURATION GUIDANCE")
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logger.info(f"{'='*60}")
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op2_files = [f for f in discovered['nastran_results'] if f['name'].endswith('.op2')]
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dat_files = [f for f in discovered['nastran_input'] if f['name'].endswith('.dat')]
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if op2_files and dat_files:
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logger.info("\n[OK] Model is ready for optimization!")
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logger.info("\nRecommended optimization_config.json settings:")
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if solution_to_use:
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logger.info(f' "solution_name": "{solution_to_use}"')
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logger.info(f' "op2_file": "{op2_files[0]["name"]}"')
|
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logger.info(f' "dat_file": "{dat_files[0]["name"]}"')
|
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logger.info(f"\n{'='*60}")
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logger.info("NEXT STEPS")
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logger.info(f"{'='*60}")
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logger.info(" 1. Review discovered files and solutions above")
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logger.info(" 2. Update optimization_config.json if needed")
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logger.info(" 3. Run validation: python run_optimization.py --validate")
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logger.info(" 4. Run test (3 trials): python run_optimization.py --test")
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logger.info(" 5. Run FEA training data collection: python run_optimization.py --train --trials 50")
|
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logger.info(" 6. Run full AtomizerField: python run_optimization.py --run --trials 100 --enable-nn")
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else:
|
|
logger.warning("\n[!] Some required files are missing!")
|
|
if not op2_files:
|
|
logger.warning(" - No OP2 file: Check solver executed correctly")
|
|
if not dat_files:
|
|
logger.warning(" - No DAT file: Check FEM export settings")
|
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|
|
return True
|
|
|
|
|
|
def run_test(config: dict, nx_solver, model_dir: Path, results_dir: Path,
|
|
study_name: str, logger, n_trials: int = 3) -> bool:
|
|
"""
|
|
Run integration test with 3 trials.
|
|
|
|
This tests:
|
|
- Multiple sequential trials complete
|
|
- NSGA-II sampling works
|
|
- Results are stored correctly
|
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|
|
Returns:
|
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True if all test trials passed, False otherwise
|
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"""
|
|
logger.info(f"\n{'='*60}")
|
|
logger.info(f"TEST MODE - {n_trials} Trial Integration Test")
|
|
logger.info(f"{'='*60}")
|
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|
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# Create temporary study for testing
|
|
storage = f"sqlite:///{results_dir / 'study_test.db'}"
|
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|
|
sampler = NSGAIISampler(population_size=5, seed=42)
|
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|
|
study = optuna.create_study(
|
|
study_name=f"{study_name}_test",
|
|
storage=storage,
|
|
sampler=sampler,
|
|
directions=['minimize', 'minimize'],
|
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load_if_exists=False
|
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)
|
|
|
|
logger.info(f"\nRunning {n_trials} test trials...")
|
|
start_time = datetime.now()
|
|
|
|
try:
|
|
study.optimize(
|
|
lambda trial: fea_objective(trial, config, nx_solver, model_dir, logger, None),
|
|
n_trials=n_trials,
|
|
show_progress_bar=True
|
|
)
|
|
|
|
elapsed = datetime.now() - start_time
|
|
n_complete = len([t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE])
|
|
n_failed = len([t for t in study.trials if t.state == optuna.trial.TrialState.FAIL])
|
|
|
|
logger.info(f"\n{'='*60}")
|
|
if n_complete == n_trials:
|
|
logger.info("TEST PASSED!")
|
|
elif n_complete > 0:
|
|
logger.info("TEST PARTIALLY PASSED")
|
|
else:
|
|
logger.info("TEST FAILED!")
|
|
logger.info(f"{'='*60}")
|
|
|
|
logger.info(f"Results:")
|
|
logger.info(f" Completed: {n_complete}/{n_trials}")
|
|
logger.info(f" Failed: {n_failed}/{n_trials}")
|
|
logger.info(f" Duration: {elapsed}")
|
|
logger.info(f" Avg time/trial: {elapsed.total_seconds()/n_trials:.1f}s")
|
|
|
|
# Show trial results
|
|
logger.info(f"\nTrial Summary:")
|
|
for trial in study.trials:
|
|
state = "OK" if trial.state == optuna.trial.TrialState.COMPLETE else "FAIL"
|
|
if trial.state == optuna.trial.TrialState.COMPLETE:
|
|
stiff = -trial.values[0]
|
|
mass = trial.values[1]
|
|
logger.info(f" Trial {trial.number}: [{state}] stiffness={stiff:.2f}, mass={mass:.4f}")
|
|
else:
|
|
logger.info(f" Trial {trial.number}: [{state}]")
|
|
|
|
if n_complete == n_trials:
|
|
logger.info(f"\n{'='*60}")
|
|
logger.info("Ready for official optimization!")
|
|
logger.info("Launch: python run_optimization.py --run --trials 100")
|
|
logger.info(f"{'='*60}")
|
|
return True
|
|
elif n_complete > 0:
|
|
logger.info(f"\n{'='*60}")
|
|
logger.info("Some trials failed. Review errors above.")
|
|
logger.info("You may proceed with caution or fix issues first.")
|
|
logger.info(f"{'='*60}")
|
|
return False
|
|
else:
|
|
logger.error(f"\nAll trials failed! Check NX connection and model setup.")
|
|
return False
|
|
|
|
except Exception as e:
|
|
logger.error(f"\nTEST FAILED with exception: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
return False
|
|
|
|
|
|
def neural_objective(trial: optuna.Trial, config: dict, surrogate: NeuralSurrogate,
|
|
model_dir: Path, logger) -> Tuple[float, float]:
|
|
"""
|
|
Neural surrogate objective function for FAST optimization.
|
|
|
|
Uses trained neural network instead of FEA - 600x+ faster!
|
|
|
|
Returns tuple: (stiffness, mass) for NSGA-II optimization
|
|
- Maximize stiffness (negate for minimize direction)
|
|
- Minimize mass
|
|
"""
|
|
# Sample design variables
|
|
design_vars = {}
|
|
for var in config['design_variables']:
|
|
param_name = var['parameter']
|
|
bounds = var['bounds']
|
|
design_vars[param_name] = trial.suggest_float(param_name, bounds[0], bounds[1])
|
|
|
|
logger.trial_start(trial.number, design_vars)
|
|
|
|
try:
|
|
# Get neural network predictions (FAST!)
|
|
prediction = surrogate.predict(design_vars)
|
|
|
|
# Extract predictions - the ParametricSurrogate predicts all objectives directly
|
|
max_displacement = prediction.get('max_displacement', 0.001)
|
|
inference_time = prediction.get('inference_time_ms', 0)
|
|
|
|
# Get predicted mass directly from neural network (if available)
|
|
# ParametricSurrogate predicts mass, frequency, displacement, and stress
|
|
mass_kg = prediction.get('mass', None)
|
|
|
|
# Calculate stiffness from predicted displacement
|
|
# Assuming fixed force of 1000N (verify from your model)
|
|
applied_force = 1000.0 # N - adjust based on your model
|
|
stiffness = applied_force / max(abs(max_displacement), 1e-6) # N/mm
|
|
|
|
# Fallback to BDF extraction if neural network doesn't predict mass
|
|
if mass_kg is None:
|
|
dat_file = model_dir / config['simulation']['dat_file']
|
|
try:
|
|
mass_kg = extract_mass_from_bdf(str(dat_file))
|
|
except Exception:
|
|
# Fallback: estimate mass from design variables
|
|
mass_kg = 0.1 # Placeholder
|
|
|
|
logger.info(f" [NEURAL] stiffness: {stiffness:.2f} N/mm, mass: {mass_kg:.4f} kg")
|
|
logger.info(f" [NEURAL] max_disp: {max_displacement:.6f} mm")
|
|
logger.info(f" [NEURAL] inference: {inference_time:.2f} ms (vs ~30s FEA)")
|
|
|
|
# Check constraints
|
|
feasible = True
|
|
mass_limit = 0.2 # kg - from config
|
|
if mass_kg > mass_limit:
|
|
feasible = False
|
|
logger.warning(f" Constraint violation: mass = {mass_kg:.4f} > {mass_limit}")
|
|
|
|
# Set user attributes
|
|
trial.set_user_attr('stiffness', stiffness)
|
|
trial.set_user_attr('mass', mass_kg)
|
|
trial.set_user_attr('max_displacement', max_displacement)
|
|
trial.set_user_attr('feasible', feasible)
|
|
trial.set_user_attr('neural_predicted', True)
|
|
trial.set_user_attr('inference_time_ms', inference_time)
|
|
|
|
objectives = {'stiffness': stiffness, 'mass': mass_kg}
|
|
logger.trial_complete(trial.number, objectives, {'mass_limit': mass_kg}, feasible)
|
|
|
|
# Return objectives for NSGA-II
|
|
# directions=['maximize', 'minimize'] -> (-stiffness, mass)
|
|
return (-stiffness, mass_kg)
|
|
|
|
except Exception as e:
|
|
logger.trial_failed(trial.number, f"Neural prediction failed: {str(e)}")
|
|
return (float('inf'), float('inf'))
|
|
|
|
|
|
def fea_objective(trial: optuna.Trial, config: dict, nx_solver: NXSolver,
|
|
model_dir: Path, logger,
|
|
training_exporter: Optional[TrainingDataExporter] = None) -> Tuple[float, float]:
|
|
"""
|
|
Multi-objective function for bracket stiffness optimization.
|
|
|
|
Returns tuple: (-stiffness, mass) for NSGA-II optimization
|
|
- Maximize stiffness (negated for minimization)
|
|
- Minimize mass
|
|
"""
|
|
# Sample design variables
|
|
design_vars = {}
|
|
for var in config['design_variables']:
|
|
param_name = var['parameter']
|
|
bounds = var['bounds']
|
|
design_vars[param_name] = trial.suggest_float(param_name, bounds[0], bounds[1])
|
|
|
|
logger.trial_start(trial.number, design_vars)
|
|
|
|
try:
|
|
# Get file paths
|
|
sim_file = model_dir / config['simulation']['sim_file']
|
|
|
|
# Run FEA simulation via NXSolver (subprocess-based)
|
|
result = nx_solver.run_simulation(
|
|
sim_file=sim_file,
|
|
working_dir=model_dir,
|
|
expression_updates=design_vars,
|
|
solution_name=config['simulation'].get('solution_name'),
|
|
cleanup=(training_exporter is None)
|
|
)
|
|
|
|
if not result['success']:
|
|
logger.trial_failed(trial.number, f"Simulation failed: {result.get('error', 'Unknown')}")
|
|
return (float('inf'), float('inf'))
|
|
|
|
# Get output files
|
|
op2_file = result['op2_file']
|
|
logger.info(f"Simulation successful: {op2_file}")
|
|
|
|
# Extract mass from BDF/DAT file
|
|
dat_file = model_dir / config['simulation']['dat_file']
|
|
mass_kg = extract_mass_from_bdf(str(dat_file))
|
|
logger.info(f" mass: {mass_kg:.4f} kg (from BDF)")
|
|
|
|
# Extract displacement from OP2 and calculate stiffness
|
|
disp_result = extract_displacement(op2_file, subcase=1)
|
|
max_displacement = disp_result['max_displacement']
|
|
|
|
# Calculate stiffness: k = F / delta
|
|
# Applied force should match your model's loading (check sim file)
|
|
applied_force = 1000.0 # N - adjust based on your model's applied load
|
|
stiffness = applied_force / max(abs(max_displacement), 1e-6) # N/mm
|
|
|
|
logger.info(f" stiffness: {stiffness:.2f} N/mm")
|
|
logger.info(f" max_displacement: {max_displacement:.6f} mm")
|
|
|
|
# Check constraints
|
|
feasible = True
|
|
constraint_results = {'mass_limit': mass_kg}
|
|
|
|
for constraint in config.get('constraints', []):
|
|
name = constraint['name']
|
|
threshold = constraint['threshold']
|
|
|
|
if name == 'mass_limit':
|
|
if mass_kg > threshold:
|
|
feasible = False
|
|
logger.warning(f" Constraint violation: mass = {mass_kg:.4f} > {threshold}")
|
|
|
|
# Set user attributes
|
|
trial.set_user_attr('stiffness', stiffness)
|
|
trial.set_user_attr('mass', mass_kg)
|
|
trial.set_user_attr('max_displacement', max_displacement)
|
|
trial.set_user_attr('feasible', feasible)
|
|
|
|
objectives = {'stiffness': stiffness, 'mass': mass_kg}
|
|
logger.trial_complete(trial.number, objectives, constraint_results, feasible)
|
|
|
|
# Export training data for AtomizerField neural network
|
|
if training_exporter is not None:
|
|
op2_path = Path(op2_file)
|
|
dat_path = op2_path.with_suffix('.dat')
|
|
|
|
export_results = {
|
|
'objectives': {'stiffness': stiffness, 'mass': mass_kg},
|
|
'constraints': constraint_results,
|
|
'max_displacement': max_displacement,
|
|
'feasible': feasible
|
|
}
|
|
|
|
simulation_files = {
|
|
'dat_file': dat_path,
|
|
'op2_file': op2_path
|
|
}
|
|
|
|
export_success = training_exporter.export_trial(
|
|
trial_number=trial.number,
|
|
design_variables=design_vars,
|
|
results=export_results,
|
|
simulation_files=simulation_files
|
|
)
|
|
|
|
if export_success:
|
|
logger.info(f" Training data exported for trial {trial.number}")
|
|
else:
|
|
logger.warning(f" Failed to export training data for trial {trial.number}")
|
|
|
|
# Return objectives for NSGA-II (maximize stiffness, minimize mass)
|
|
# Using directions=['minimize', 'minimize'] with -stiffness
|
|
return (-stiffness, mass_kg)
|
|
|
|
except Exception as e:
|
|
logger.trial_failed(trial.number, str(e))
|
|
return (float('inf'), float('inf'))
|
|
|
|
|
|
def main():
|
|
"""Main optimization workflow with staged validation and neural surrogate integration."""
|
|
|
|
parser = argparse.ArgumentParser(
|
|
description='Bracket Stiffness Optimization with AtomizerField Neural Acceleration',
|
|
formatter_class=argparse.RawDescriptionHelpFormatter,
|
|
epilog="""
|
|
Staged Workflow (recommended order):
|
|
1. --discover Clean old files, run ONE solve, discover outputs
|
|
2. --validate Run single trial to validate extraction works
|
|
3. --test Run 3 trials as integration test
|
|
4. --train Run FEA trials for training data collection
|
|
5. --run Launch official optimization (with --enable-nn for neural)
|
|
|
|
Examples:
|
|
python run_optimization.py --discover
|
|
python run_optimization.py --validate
|
|
python run_optimization.py --test
|
|
python run_optimization.py --train --trials 50
|
|
python run_optimization.py --run --trials 100 --enable-nn --resume
|
|
"""
|
|
)
|
|
|
|
# Workflow stage selection (mutually exclusive)
|
|
stage_group = parser.add_mutually_exclusive_group()
|
|
stage_group.add_argument('--discover', action='store_true',
|
|
help='Stage 1: Clean files, run ONE solve, discover outputs')
|
|
stage_group.add_argument('--validate', action='store_true',
|
|
help='Stage 2: Run single validation trial')
|
|
stage_group.add_argument('--test', action='store_true',
|
|
help='Stage 3: Run 3-trial integration test')
|
|
stage_group.add_argument('--train', action='store_true',
|
|
help='Stage 4: Run FEA trials for training data')
|
|
stage_group.add_argument('--run', action='store_true',
|
|
help='Stage 5: Launch official optimization')
|
|
|
|
# Common options
|
|
parser.add_argument('--trials', type=int, default=100,
|
|
help='Number of optimization trials (default: 100)')
|
|
parser.add_argument('--resume', action='store_true',
|
|
help='Resume from existing study')
|
|
parser.add_argument('--enable-nn', action='store_true',
|
|
help='Enable neural surrogate (requires trained model)')
|
|
parser.add_argument('--no-export', action='store_true',
|
|
help='Disable training data export')
|
|
parser.add_argument('--clean', action='store_true',
|
|
help='Clean old Nastran files before running')
|
|
|
|
args = parser.parse_args()
|
|
|
|
# Default to --run if no stage specified
|
|
if not any([args.discover, args.validate, args.test, args.train, args.run]):
|
|
print("No workflow stage specified. Use --discover, --validate, --test, --train, or --run")
|
|
print("Run with --help for usage information")
|
|
return 1
|
|
|
|
# Setup paths
|
|
study_dir = Path(__file__).parent
|
|
config_path = study_dir / "1_setup" / "optimization_config.json"
|
|
model_dir = study_dir / "1_setup" / "model"
|
|
results_dir = study_dir / "2_results"
|
|
results_dir.mkdir(exist_ok=True)
|
|
|
|
study_name = "bracket_stiffness_optimization_atomizerfield"
|
|
|
|
# Initialize logger
|
|
logger = get_logger(study_name, study_dir=results_dir)
|
|
|
|
# Load config
|
|
config = load_config(config_path)
|
|
|
|
# Initialize NX Solver (deferred - only when needed)
|
|
# For neural-only mode, we don't need NX at all
|
|
nx_solver = None
|
|
|
|
def get_nx_solver():
|
|
"""Lazily initialize NX solver when needed."""
|
|
nonlocal nx_solver
|
|
if nx_solver is None:
|
|
nx_solver = NXSolver()
|
|
return nx_solver
|
|
|
|
# Optional clean before any stage
|
|
if args.clean:
|
|
clean_nastran_files(model_dir, logger)
|
|
|
|
# =========================================================================
|
|
# STAGE 1: DISCOVER - Clean, run one solve, discover outputs
|
|
# =========================================================================
|
|
if args.discover:
|
|
logger.info(f"\n{'='*60}")
|
|
logger.info("STAGE 1: DISCOVER")
|
|
logger.info(f"{'='*60}")
|
|
success = run_discovery(config, get_nx_solver(), model_dir, results_dir, study_name, logger)
|
|
return 0 if success else 1
|
|
|
|
# =========================================================================
|
|
# STAGE 2: VALIDATE - Run single trial to validate extraction
|
|
# =========================================================================
|
|
if args.validate:
|
|
logger.info(f"\n{'='*60}")
|
|
logger.info("STAGE 2: VALIDATE")
|
|
logger.info(f"{'='*60}")
|
|
success = run_validation(config, get_nx_solver(), model_dir, results_dir, study_name, logger)
|
|
return 0 if success else 1
|
|
|
|
# =========================================================================
|
|
# STAGE 3: TEST - Run 3 trials as integration test
|
|
# =========================================================================
|
|
if args.test:
|
|
logger.info(f"\n{'='*60}")
|
|
logger.info("STAGE 3: TEST")
|
|
logger.info(f"{'='*60}")
|
|
success = run_test(config, get_nx_solver(), model_dir, results_dir, study_name, logger, n_trials=3)
|
|
return 0 if success else 1
|
|
|
|
# =========================================================================
|
|
# STAGE 4: TRAIN - Run FEA trials for training data collection
|
|
# =========================================================================
|
|
if args.train:
|
|
logger.info(f"\n{'='*60}")
|
|
logger.info("STAGE 4: TRAIN - FEA Data Collection")
|
|
logger.info(f"{'='*60}")
|
|
# Force training data export for this stage
|
|
args.no_export = False
|
|
|
|
# =========================================================================
|
|
# STAGE 5: RUN - Official optimization (FEA or Neural)
|
|
# =========================================================================
|
|
# (args.run or args.train fall through to here)
|
|
|
|
# Check neural surrogate status
|
|
neural_enabled = args.enable_nn
|
|
surrogate = None
|
|
|
|
if neural_enabled:
|
|
logger.info("Neural surrogate mode requested")
|
|
|
|
try:
|
|
surrogate = create_surrogate_for_study(project_root=project_root)
|
|
if surrogate is not None:
|
|
logger.info(f"Neural surrogate loaded successfully!")
|
|
logger.info(f" Model: {surrogate.model_path}")
|
|
logger.info(f" Device: {surrogate.device}")
|
|
logger.info(f" Expected speedup: 600x+ over FEA")
|
|
else:
|
|
logger.warning("Neural surrogate not available - falling back to FEA")
|
|
neural_enabled = False
|
|
except Exception as e:
|
|
logger.warning(f"Failed to initialize neural surrogate: {e}")
|
|
logger.warning("Falling back to FEA mode")
|
|
neural_enabled = False
|
|
|
|
# Initialize training data exporter
|
|
training_exporter = None
|
|
export_config = config.get('training_data_export', {})
|
|
|
|
# Enable export for --train stage or if config says so
|
|
should_export = (args.train or export_config.get('enabled', False)) and not args.no_export and not neural_enabled
|
|
|
|
if should_export:
|
|
export_dir = export_config.get('export_dir', f'atomizer_field_training_data/{study_name}')
|
|
if not Path(export_dir).is_absolute():
|
|
export_dir = project_root / export_dir
|
|
|
|
design_var_names = [dv['parameter'] for dv in config.get('design_variables', [])]
|
|
objective_names = [obj['name'] for obj in config.get('objectives', [])]
|
|
constraint_names = [c['name'] for c in config.get('constraints', [])]
|
|
|
|
training_exporter = TrainingDataExporter(
|
|
export_dir=export_dir,
|
|
study_name=study_name,
|
|
design_variable_names=design_var_names,
|
|
objective_names=objective_names,
|
|
constraint_names=constraint_names,
|
|
metadata={
|
|
'atomizer_version': '2.0',
|
|
'optimization_algorithm': 'NSGA-II',
|
|
'n_trials': args.trials,
|
|
'description': config.get('description', 'Bracket stiffness optimization')
|
|
}
|
|
)
|
|
logger.info(f"Training data export enabled: {export_dir}")
|
|
else:
|
|
logger.info("Training data export disabled")
|
|
|
|
# Create Optuna study (multi-objective)
|
|
storage = f"sqlite:///{results_dir / 'study.db'}"
|
|
|
|
sampler = NSGAIISampler(
|
|
population_size=20,
|
|
mutation_prob=0.1,
|
|
crossover_prob=0.9,
|
|
seed=42
|
|
)
|
|
|
|
stage_name = "TRAIN" if args.train else "RUN"
|
|
logger.study_start(study_name, args.trials, "NSGAIISampler")
|
|
|
|
if args.resume:
|
|
study = optuna.load_study(
|
|
study_name=study_name,
|
|
storage=storage,
|
|
sampler=sampler
|
|
)
|
|
logger.info(f"Resumed study with {len(study.trials)} existing trials")
|
|
else:
|
|
study = optuna.create_study(
|
|
study_name=study_name,
|
|
storage=storage,
|
|
sampler=sampler,
|
|
directions=['minimize', 'minimize'], # -stiffness, mass
|
|
load_if_exists=True
|
|
)
|
|
|
|
# Run optimization
|
|
logger.info(f"\n{'='*60}")
|
|
if args.train:
|
|
logger.info("STAGE 4: TRAIN - FEA Training Data Collection")
|
|
logger.info("Collecting FEA data for AtomizerField neural network")
|
|
elif neural_enabled and surrogate is not None:
|
|
logger.info("STAGE 5: RUN - Neural Accelerated Optimization")
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|
logger.info("Using trained neural network for FAST predictions!")
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|
else:
|
|
logger.info("STAGE 5: RUN - FEA Optimization")
|
|
logger.info(f"Trials: {args.trials}")
|
|
logger.info(f"Neural Surrogate: {'ENABLED - 600x+ speedup!' if neural_enabled else 'Disabled'}")
|
|
logger.info(f"Training Export: {'ENABLED' if training_exporter else 'Disabled'}")
|
|
logger.info(f"{'='*60}\n")
|
|
|
|
start_time = datetime.now()
|
|
|
|
try:
|
|
if neural_enabled and surrogate is not None:
|
|
study.optimize(
|
|
lambda trial: neural_objective(trial, config, surrogate, model_dir, logger),
|
|
n_trials=args.trials,
|
|
show_progress_bar=True
|
|
)
|
|
else:
|
|
# FEA mode - need NX solver
|
|
solver = get_nx_solver()
|
|
study.optimize(
|
|
lambda trial: fea_objective(trial, config, solver, model_dir, logger, training_exporter),
|
|
n_trials=args.trials,
|
|
show_progress_bar=True
|
|
)
|
|
|
|
elapsed = datetime.now() - start_time
|
|
n_successful = len([t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE])
|
|
|
|
logger.study_complete(study_name, len(study.trials), n_successful)
|
|
|
|
# Report results
|
|
logger.info(f"\n{'='*60}")
|
|
logger.info(f"Optimization Complete")
|
|
logger.info(f"{'='*60}")
|
|
logger.info(f"Duration: {elapsed}")
|
|
logger.info(f"Total trials: {len(study.trials)}")
|
|
logger.info(f"Successful: {n_successful}")
|
|
|
|
if neural_enabled and surrogate is not None:
|
|
avg_time_per_trial_ms = (elapsed.total_seconds() * 1000) / max(n_successful, 1)
|
|
estimated_fea_time = n_successful * 30
|
|
actual_time = elapsed.total_seconds()
|
|
speedup = estimated_fea_time / max(actual_time, 0.001)
|
|
logger.info(f"\n [NEURAL PERFORMANCE]")
|
|
logger.info(f" Avg time per trial: {avg_time_per_trial_ms:.1f} ms")
|
|
logger.info(f" Estimated FEA time: {estimated_fea_time:.0f} seconds ({estimated_fea_time/60:.1f} min)")
|
|
logger.info(f" Actual neural time: {actual_time:.1f} seconds")
|
|
logger.info(f" SPEEDUP: {speedup:.0f}x faster!")
|
|
|
|
# Show Pareto front
|
|
pareto_trials = study.best_trials
|
|
logger.info(f"\nPareto Front ({len(pareto_trials)} solutions):")
|
|
for i, trial in enumerate(pareto_trials[:5]):
|
|
stiffness = -trial.values[0] # Convert back from negated
|
|
mass = trial.values[1]
|
|
feasible = trial.user_attrs.get('feasible', 'N/A')
|
|
logger.info(f" {i+1}. Stiffness: {stiffness:.2f} N/mm, Mass: {mass:.4f} kg, Feasible: {feasible}")
|
|
|
|
# Finalize training data export
|
|
if training_exporter is not None:
|
|
training_exporter.finalize()
|
|
logger.info(f"Training data finalized: {training_exporter.trial_count} trials exported")
|
|
|
|
# Next steps
|
|
if not neural_enabled and training_exporter is not None:
|
|
logger.info(f"\n{'='*60}")
|
|
logger.info("Next Steps for Neural Acceleration")
|
|
logger.info(f"{'='*60}")
|
|
logger.info(f"1. Training data collected: {training_exporter.export_dir}")
|
|
logger.info(f" Exported {training_exporter.trial_count} trials")
|
|
logger.info("2. Parse training data for neural network:")
|
|
logger.info(" cd atomizer-field")
|
|
logger.info(f" python batch_parser.py {training_exporter.export_dir}")
|
|
logger.info("3. Train neural network:")
|
|
logger.info(" python train.py --epochs 200")
|
|
logger.info("4. Re-run with neural surrogate:")
|
|
logger.info(" python run_optimization.py --trials 50 --enable-nn --resume")
|
|
|
|
except Exception as e:
|
|
if training_exporter is not None:
|
|
training_exporter.finalize()
|
|
logger.error(f"Optimization failed: {e}", exc_info=True)
|
|
raise
|
|
|
|
return 0
|
|
|
|
|
|
if __name__ == "__main__":
|
|
exit(main())
|