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arXiv · 2211.06400

Prior-mean-assisted Bayesian optimization application on FRIB Front-End tunning

Kilean Hwang·Tomofumi Maruta·Alexander Plastun·Kei Fukushima·Tong Zhang·Qiang Zhao·Peter Ostroumov·Yue Hao
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Abstract

Bayesian optimization~(BO) is often used for accelerator tuning due to its high sample efficiency. However, the computational scalability of training over large data-set can be problematic and the adoption of historical data in a computationally efficient way is not trivial. Here, we exploit a neural network model trained over historical data as a prior mean of BO for FRIB Front-End tuning.

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Kilean Hwang, Tomofumi Maruta, Alexander Plastun, Kei Fukushima, Tong Zhang, Qiang Zhao, Peter Ostroumov, Yue Hao. 2022-11-11. Prior-mean-assisted Bayesian optimization application on FRIB Front-End tunning. https://arxiv.org/abs/2211.06400

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