Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization
Abstract
Bayesian Optimization is a useful tool for experiment design. Unfortunately, the classical, sequential setting of Bayesian Optimization does not translate well into laboratory experiments, for instance battery design, where measurements may come from different sources and their evaluations may require significant waiting times. Multi-fidelity Bayesian Optimization addresses the setting with measurements from different sources. Asynchronous batch Bayesian Optimization provides a framework to select new experiments before the results of the prior experiments are revealed. This paper proposes an algorithm combining multi-fidelity and asynchronous batch methods. We empirically study the algorithm behavior, and show it can outperform single-fidelity batch methods and multi-fidelity sequential methods. As an application, we consider designing electrode materials for optimal performance in pouch cells using experiments with coin cells to approximate battery performance.
Cite
@article{arxiv.2211.06149,
title = {Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization},
author = {Jose Pablo Folch and Robert M Lee and Behrang Shafei and David Walz and Calvin Tsay and Mark van der Wilk and Ruth Misener},
journal= {arXiv preprint arXiv:2211.06149},
year = {2023}
}
Comments
19 pages in main paper / 28 with references and appendix, 7 figures, 2 tables, accepted into Computers and Chemical Engineering