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

• URL: http://arxiv.org/abs/2211.06400v1
• Date: Fri, 11 Nov 2022 18:34:15 GMT
• Title: Prior-mean-assisted Bayesian optimization application on FRIB Front-End tunning
• Authors: Kilean Hwang, Tomofumi Maruta, Alexander Plastun, Kei Fukushima, Tong Zhang, Qiang Zhao, Peter Ostroumov, Yue Hao
• Abstract summary: We exploit a neural network model trained over historical data as a prior mean of BO for FRIB Front-End tuning. In this paper, we exploit a neural network model trained over historical data as a prior mean of BO for FRIB Front-End tuning.
• Score: 61.78406085010957
• License: http://creativecommons.org/licenses/by/4.0/
• 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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