domains / families / science / parameter-optimization

parameter-optimization

separate

Explore and optimize simulation parameters via design of experiments (DOE), sensitivity analysis, and optimizer selection. Use for calibration, uncertainty studies, parameter sweeps, LHS sampling, Sobol analysis, surrogate modeling, or Bayesian optimization setup.

When to use

• calibration
• uncertainty studies
• parameter sweeps
• LHS sampling
• Sobol analysis
• surrogate modeling
• Bayesian optimization setup
• design of experiments

When not to use

• **Not for real-time optimization**: Scripts provide recommendations, not live optimization loops
• **Surrogate is a placeholder**: `surrogate_builder.py` computes basic metrics; replace with actual model for production
• **No automatic simulation execution**: User must run simulations externally and provide results

Skill page

Source