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.

翻译：贝叶斯优化是实验设计的一种有效工具。然而，经典的序贯式贝叶斯优化设置难以直接应用于实验室实验，例如电池设计，其中测量数据可能来自不同来源，且评估可能需要较长的等待时间。多保真度贝叶斯优化针对来自不同来源的测量数据场景。异步批量贝叶斯优化则提供了一种框架，允许在先验实验结果公布之前选择新实验。本文提出了一种结合多保真度与异步批量方法的算法。我们通过实验研究了该算法的行为，并证明其性能优于单保真度批量方法和多保真度序贯方法。作为应用实例，我们考虑使用纽扣电池实验近似电池性能，以设计用于软包电池最优性能的电极材料。