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PHYSBO (optimization tools for PHYsics based on Bayesian Optimization )

  • 公開度：3 ★★★
  • ドキュメント充実度：2 ★★☆

PHYSBO is a Python library for researchers mainly in the materials science field to perform fast and scalable Bayesian optimization based on COMBO (Common Bayesian Optimization). Users can search the candidate with the largest objective function value from candidates listed in advance by using machine learning prediction. PHYSBO can handle a larger amount of data compared with standard implementations such as scikit-learn.

基本情報
Last Update:2024/07/18

公式サイト

https://www.pasums.issp.u-tokyo.ac.jp/physbo/en/

ライセンス

Mozilla Public License v. 2.0

利用環境
  • Python >= 3.6
  • numpy
  • scipy
開発者

Ryo Tamura (International Center for Materials Nanoarchitectonics, National Institute for Materials Science)
Kei Terayama (Graduate School of Medical Life Science, Yokohama City University)
Tsuyoshi Ueno (Magne-Max Capital Management Company)
Koji Tsuda(Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo.)
Yuichi Motoyama (Institute for Solid State Physics, The University of Tokyo)
Kazuyoshi Yoshimi (Institute for Solid State Physics, The University of Tokyo)
Naoki Kawashima (Institute for Solid State Physics, The University of Tokyo)
手法

Bayesian optimization
関連アプリ
関連キーワード
文献

Tsuyoshi Ueno, Trevor David Rhone, Zhufeng Hou, Teruyasu Mizoguchi and Koji Tsuda,
COMBO: An Efficient Bayesian Optimization Library for Materials Science,
Materials Discovery 4, 18-21 (2016).

Yuichi Motoyama, Ryo Tamura, Kazuyoshi Yoshimi, Kei Terayama, Tsuyoshi Ueno, Koji Tsuda,
Bayesian optimization package: PHYSBO
Comput. Phys. Commun. 278, 108405 (2022).
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