.png){kind=icon}
Return
Combining multi-fidelity modelling and asynchronous batch Bayesian Optimization
.png){kind=icon}
OA
AI
DOI：10.1016/j.compchemeng.2023.108194.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Bayesian Optimization
Machine learning
Batch optimization
Asynchronous
Multi-fidelity
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
C
IF:
3.9
Papers:
8.1K
Citations:
1.7W
Organization
Cited Papers
ShareSaveShareSave
Critical Parameters for Evaluating Coin Cells and Pouch Cells of Rechargeable Li-Metal Batteries.png)
JOULEOAAI
JOULEIF35.4
OAAIShareSaveTaking the Human Out of the Loop: A Review of Bayesian OptimizationPROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
ShareSaveShareSave
View more