A Bayesian global optimization package for material design
Project description
[![](https://img.shields.io/badge/PyPI-caobin-blue)](https://pypi.org/project/Bgolearn/) # Python package - Bgolearn
![Screen Shot 2022-07-11 at 9 13 28 AM](https://user-images.githubusercontent.com/86995074/178176016-8a79db81-fcfb-4af0-9b1c-aa4e6a113b5e.png)
## 为材料设计而生! ## ( A Bayesian global optimization package for material design )Version 1, Jul, 2022
Reference paper : V. Picheny, T. Wagner, and D. Ginsbourger. “A Benchmark of Kriging-Based Infill Criteria for Noisy Optimization”. In: Structural and Multidisciplinary Optimization 48.3 (Sept. 2013), pp. 607–626. issn: 1615-1488.
Written using Python, which is suitable for operating systems, e.g., Windows/Linux/MAC OS etc.
## Content Bgolearn guides subsequent material design based on existed experimental data. Which includes: 1.Expected Improvement algorithm, 2.Expected improvement with “plugin”,3.Augmented Expected Improvement,4.Expected Quantile Improvement,5.Reinterpolation Expected Improvement, 6.Upper confidence bound,7.Probability of Improvement,8.Predictive Entropy Search,9.Knowledge Gradient, a total of nine Utility Functions. Predictive Entropy Search,Knowledge Gradient are implemented based on Monte Carlo simulation.(贝叶斯优化设计,根据已有的实验数据对后续材料设计作出指导,本算法包共包括:期望最大化算法,期望最大化算法改进(考虑数据噪声),上确界方法,期望提升方法,熵搜索,知识梯度方法等在内的共计9种贝叶斯采样方法。其中熵搜索和知识梯度方法基于蒙特卡洛实现)
## Installing / 安装 pip install Bgolearn
## Updating / 更新 pip install –upgrade Bgolearn
## About / 更多 Maintained by Bin Cao. Please feel free to open issues in the Github or contact Bin Cao (bcao@shu.edu.cn) in case of any problems/comments/suggestions in using the code.
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