Prior-mean-assisted Bayesian optimization application on FRIB Front-End tunning. (arXiv:2211.06400v1 [physics.acc-ph])

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.

application arxiv bayesian front-end mean optimization physics prior