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parameter-optimization

Maintainer FreedomIntelligence · Last updated April 1, 2026

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.

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Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/parameter-optimization

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

Provide a workflow to design experiments, rank parameter influence, and select optimization strategies for materials simulation calibration.
python3 scripts/doe_generator.py --params 3 --budget 20 --method lhs --json.

Source Doc

Excerpt From SKILL.md

Requirements

Python 3.8+
No external dependencies (uses Python standard library only)

Inputs to Gather

Before running any scripts, collect from the user:

Input	Description	Example
Parameter bounds	Min/max for each parameter with units	`kappa: [0.1, 10.0] W/mK`
Evaluation budget	Max number of simulations allowed	`50 runs`
Noise level	Stochasticity of simulation outputs	`low`, `medium`, `high`
Constraints	Feasibility rules or forbidden regions	`kappa + mobility < 5`

Choosing a DOE Method

Method	Best For	Avoid When
`lhs`	General exploration, moderate dimensions (3-20)	Need exact grid coverage
`sobol`	Sensitivity analysis, uniform coverage	Very high dimensions (>20)
`factorial`	Low dimension (<4), need all corners	High dimension (exponential growth)

Use cases

Use for calibration, uncertainty studies, parameter sweeps, LHS sampling, Sobol analysis, surrogate modeling, or Bayesian optimization setup.

Not for

Do not rely on this catalog entry alone for installation or maintenance details.

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