Combining multi-fidelity modelling and asynchronous batch Bayesian Optimization
Combining multi-fidelity modelling and asynchronous batch Bayesian Optimization
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DOI：
10.1016/j.compchemeng.2023.108194
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发表时间：
2023-03-02
影响因子：
4.3
通讯作者：
Misener, Ruth
中科院分区：
文献类型：
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作者：
Folch, Jose Pablo;Lee, Robert M.;Misener, Ruth
贝叶斯优化是一种有用的实验设计工具。不幸的是，经典的贝叶斯优化的顺序设置不能很好地转化为实验室实验，例如电池设计，其中测量可能来自不同的来源，其评估可能需要很长的等待时间。多保真贝叶斯优化解决了来自不同来源的测量的设置问题。异步批处理贝叶斯优化提供了一个框架，用于在先前实验的结果显示之前选择新的实验。提出了一种多保真和异步批处理相结合的算法。我们对算法的性能进行了实证研究，结果表明，该算法的性能优于单保真批处理方法和多保真序列方法。作为一个应用，我们考虑设计电极材料，通过使用硬币电池来接近电池性能的实验，使其在邮袋电池中具有最佳性能。
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 behaviour, 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.