PHYSBOについて

@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},
keywords = {Bayesian optimization, Multi-objective optimization, Materials screening, Effective model estimation},
abstract = {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.
Program summary
Program Title: PHYSBO CPC Library link to program files: https://doi.org/10.17632/22d72yb6k6.1 Developer's repository link: https://github.com/issp-center-dev/PHYSBO Licensing provisions: GNU General Public License version 3 Programming language: Python3 External routines/libraries: NumPy, SciPy, MPI for Python. Nature of problem: Bayesian optimization (BO) can be used to select inputs that will yield better outputs from a list of candidate inputs with the help of machine learning prediction through a Gaussian process. Although BO is a powerful tool, two of its components, training the Gaussian process regression and optimizing the acquisition function, are generally computationally expensive. Moreover, hyperparameter tuning is necessary for the former process. Solution method: PHYSBO is a Python library for performing fast and scalable Bayesian optimization. To avoid the computationally expensive training process, PHYSBO uses a random feature map, Thompson sampling, and a one-rank Cholesky update. In addition, PHYSBO performs hyperparameter tuning automatically by maximizing the Type II likelihood, and MPI parallelization is used to reduce the calculation time for optimizing the acquisition function.}
}