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design-of-experiments

Expert guidance for Design of Experiments (DOE) in Python - interactive goal-driven design selection, classical DOE (factorial, response surface, screening), Bayesian optimization with Gaussian processes, model-driven optimal designs, active learning, and sequential experimentation; includes pyDOE3, pycse, GPyOpt, scikit-optimize, statsmodels

jkitchin☆ 14 仓库 Stars2 仓库 Forks

安装与使用

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技能说明

功能与用途

Expert guidance for Design of Experiments (DOE) in Python - interactive goal-driven design selection, classical DOE (factorial, response surface, screening), Bayesian optimization with Gaussian processes, model-driven optimal designs, active learning, and sequential experimentation; includes pyDOE3, pycse, GPyOpt, scikit-optimize, statsmodels

快速开始

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。

使用指南 ↗

使用示例

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来源历史

收录时间
2026-01-20
索引更新时间
2026-02-06
Branch
main

收录与同步日期属于本站记录。源码修改历史请查看来源仓库。

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