Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization
This addresses experiment design challenges in domains like battery design, where measurements vary in source and time, but it is incremental as it builds on existing methods.
The paper tackles the problem of optimizing experiments with multiple data sources and waiting times by combining multi-fidelity and asynchronous batch Bayesian optimization, showing it can outperform single-fidelity batch and multi-fidelity sequential methods in empirical studies.
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.