论文标题
在FRIB前端调音上先前辅助贝叶斯优化应用
Prior-mean-assisted Bayesian optimization application on FRIB Front-End tunning
论文作者
论文摘要
贝叶斯优化〜（BO）由于其较高的样品效率而通常用于加速器调整。但是，大型数据集对培训的计算可伸缩性可能是有问题的，并且以计算有效的方式采用历史数据并不是微不足道的。在这里，我们利用了一个基于历史数据训练的神经网络模型，作为FRIB前端调整的BO的先前平均值。
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.
