KADAL | Kriging代理模型Python工具箱推荐
用于分析、设计优化和探索的Kriging工具箱
- 简介
- Required packages
- Quick Examples
- Contact
- 特别感谢：
- 参考资料
简介
用于分析、设计优化和探索的克里金法 (Kriging for Analysis, Design optimization, And expLoration, KADAL) 是由万隆理工学院 (Institut Teknologi Bandung, ITB) 的流动诊断研究小组（Flow Diagnostics Research Group）[1]开发的 Python 程序，其中包含贝叶斯优化工具集合，包括各种代理模型方法、采样技术和优化方法。目前，该程序正在开发中，尚未在 Pypi 上提供。该程序的覆盖范围仍然仅限于：
- Kriging
Ordinary Kriging
Regression Kriging
Polynomial Kriging
Composite Kernel Kriging
Kriging with Partial Least Square - Bayesian Optimization
Unconstrained Single-Objective Bayesian Optimization (Expected Improvement)
Unconstrained Multi-Objective Bayesian Optimization (ParEGO, EHVI) - Reliability Analysis
AK-MCS - Sensitivity Analysis
Sobol indices
Required packages
需要以下第三方库：
- numpy
- scipy
- matplotlib
- sobolsampling
- scikit-learn
- cma
KADAL 已在 Python 3.6.1 上测试
Quick Examples
演示代码位于主文件夹中：
- KrigDemo.py is a demo script for creating surrogate model.
- MOBOdemo.py is a demo script for performing unconstrained multi-objective optimization for Schaffer test function.
- SOBOdemo.py is a demo script for performing unconstrained single objective optimization for Branin test function.
Contact
原始程序由 Pramudita Satria Palar、Kemas Zakaria 和 Ghifari Adam Faza 编写，并由空气动力学研究组 ITB 维护。
e-mail: pramsp@ftmd.itb.ac.id
特别感谢：
Timothy Jim (Tohoku University)
Potsawat Boonjaipetch (Tohoku University)
参考资料
[1] Their Lab’s Homepage: https://flowdiagnostics.ftmd.itb.ac.id