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parameter-optimization

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Plan and optimize parameter studies with DOE.

Data & Analytics#optimization#sampling#sensitivity#doe#design-of-experiments#surrogate
AuthorHeshamFS
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This Skill provides a structured workflow for experimental design (DOE), sensitivity analysis, and optimizer selection to calibrate models efficiently.

Core Features & Use Cases

  • DOE generation: create sampling plans using LHS, Sobol, or factorial designs.
  • Optimizer selection: pick Bayesian, CMA-ES, or others based on problem size and noise.
  • Surrogate modeling: quick fit and evaluation of surrogate models.
  • Practical workflow: end-to-end from DOE to surrogate to optimization.

Quick Start

Create 20 LHS samples for 4 parameters: python3 scripts/doe_generator.py --params 4 --budget 20 --method lhs --json

Dependency Matrix

Required Modules

None required

Components

scriptsreferences

💻 Claude Code Installation

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Please help me install this Skill:
Name: parameter-optimization
Download link: https://github.com/HeshamFS/materials-simulation-skills/archive/main.zip#parameter-optimization

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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