PHYSBO Gallery

概要

PHYSBO (optimization tools for PHYSics based on Bayesian Optimization) is a Python library for fast and scalable Bayesian optimization. It has been developed mainly for application in the basic sciences such as physics and materials science. Bayesian optimization is used to select an appropriate input for experiments/simulations from candidate inputs listed in advance in order to obtain better output values with the help of machine learning prediction. PHYSBO can be used to find better solutions for both single and multi-objective optimization problems. At each cycle in the Bayesian optimization, a single proposal or multiple proposals can be obtained for the next experiments/simulations. These proposals can be obtained interactively for use in experiments. PHYSBO is available at https://github.com/issp-center-dev/PHYSBO. This repository provides the sample data including input and output data for PHYSBO.

URL: https://isspns-gitlab.issp.u-tokyo.ac.jp/physbo-dev/physbo-gallery
DOI: 10.1016/j.cpc.2022.108405
被引用数: 99 件（OpenAlex、2026-10-05 時点）
著者: Ryo Tamura, Yuichi Motoyama, Kazuyoshi Yoshimi, Kei Terayama, Tsuyoshi Ueno, Koji Tsuda
ライセンス: Documents: CC BY 4.0, Codes: GNU General Public License v3.0 or later
キーワード: Bayesian optimizationGaussian process regressionrandom feature mapsThompson samplinghyperparameter optimizationmulti-objective optimizationscalable optimizationphysics experiments（論文のキーワード、OpenAlex より）
カテゴリ: 機械学習
関連文献: Computer Physics Communications Volume 278, September 2022, 108405
関連ソフトウェア: PHYSBO (リンク先: PASUMS ソフトウェアカタログ)
連絡先: physbo-dev__at__issp.u-tokyo.ac.jp (__at__ を @ に置き換えてください。)

論文と引用

このデータを利用した場合は、次の論文を引用してください。

Yuichi Motoyama et al., "Bayesian optimization package: PHYSBO", Computer Physics Communications 278, 108405 (2022). https://doi.org/10.1016/j.cpc.2022.108405

論文を読む（オープンアクセス） ↗

BibTeX を表示

@article{Motoyama_2022, title={Bayesian optimization package: PHYSBO}, volume={278}, ISSN={0010-4655}, url={http://dx.doi.org/10.1016/j.cpc.2022.108405}, DOI={10.1016/j.cpc.2022.108405}, journal={Computer Physics Communications}, publisher={Elsevier BV}, author={Motoyama, Yuichi and Tamura, Ryo and Yoshimi, Kazuyoshi and Terayama, Kei and Ueno, Tsuyoshi and Tsuda, Koji}, year={2022}, month=Sept, pages={108405} }

書誌情報とオープンアクセスのリンクは OpenAlex、BibTeX は doi.org（出版社が登録した情報）から取得しています。

リンク

💻 リポジトリを開く 📦 ZIP でダウンロード

README

About

PHYSBO (optimization tools for PHYSics based on Bayesian Optimization) is a Python library for fast and scalable Bayesian optimization. It has been developed mainly for application in the basic sciences such as physics and materials science. Bayesian optimization is used to select an appropriate input for experiments/simulations from candidate inputs listed in advance in order to obtain better output values with the help of machine learning prediction. PHYSBO can be used to find better solutions for both single and multi-objective optimization problems. At each cycle in the Bayesian optimization, a single proposal or multiple proposals can be obtained for the next experiments/simulations. These proposals can be obtained interactively for use in experiments. PHYSBO is available at https://github.com/issp-center-dev/PHYSBO. This repository provides the sample data including input and output data for PHYSBO.

License

Documents: CC BY 4.0
Source codes:
- tutorial: Mozilla Public License Version 2.0
- tools/physbo_exe: Mozilla Public License Version 2.0
- data/paper
  - cpc_2022: GNU General Public License v3.0 or later
  - stam_2025: MIT License
  - v3_2026: Mozilla Public License Version 2.0

We hope that you cite the reference when you publish the results using PHYSBO, our data or codes:

Yuichi Motoyama, Ryo Tamura, Kazuyoshi Yoshimi, Kei Terayama, Tsuyoshi Ueno, Koji Tsuda, "Bayesian optimization package: PHYSBO", Computer Physics Comunications 278, 108405(2022).

Bibtex:

@article{MOTOYAMA2022108405,
title = {Bayesian optimization package: PHYSBO},
journal = {Computer Physics Communications},
volume = {278},
pages = {108405},
year = {2022},
issn = {0010-4655},
doi = {https://doi.org/10.1016/j.cpc.2022.108405},
url = {https://www.sciencedirect.com/science/article/pii/S0010465522001242},
author = {Yuichi Motoyama and Ryo Tamura and Kazuyoshi Yoshimi and Kei Terayama and Tsuyoshi Ueno and Koji Tsuda}
}

Folder structures

LICENSE
README.md
data
- paper
  - cpc_2022
  - stam_2025
  - v3_2026
- tutorial
  - 01.basic
  - 02.Guassian_process
  - 03.interactive_mode
  - ...
tools
- physbo_exe
  - physbo.exe.zip
  - PHYSBO_exe_manual_en.md
  - PHYSBO_exe_manual_prev.md
  - candidates.csv

Contact

physbo-dev__at__issp.u-tokyo.ac.jp

please replace __at__ by @.

出典: https://isspns-gitlab.issp.u-tokyo.ac.jp/physbo-dev/physbo-gallery の README

更新状況

更新最終更新: 2026-09-29 (5 日前)