论文标题
粒子加速器喷射器调整的神经网络先验平均值
Neural Network Prior Mean for Particle Accelerator Injector Tuning
论文作者
论文摘要
贝叶斯优化已被证明是在线加速器优化期间解决黑匣子问题的强大工具。基于贝叶斯的优化技术的主要优点是能够包括有关该问题的先验信息以加快优化，即使该信息与实验测量不完全相关。同时，越来越多地提供加速器设施的神经网络替代系统模型，但目前尚未广泛用于在线优化中。在这项工作中，我们证明了使用近似神经网络替代模型作为在现实环境中贝叶斯优化中使用的高斯过程的先前平均值。我们表明，即使替代模型做出了错误的预测，也可以使用神经网络替代模型来改善贝叶斯优化的初始性能。最后，我们量化对替代预测准确性的要求，以在解决高维输入空间中的问题时达到优化性能。
Bayesian optimization has been shown to be a powerful tool for solving black box problems during online accelerator optimization. The major advantage of Bayesian based optimization techniques is the ability to include prior information about the problem to speed up optimization, even if that information is not perfectly correlated with experimental measurements. In parallel, neural network surrogate system models of accelerator facilities are increasingly being made available, but at present they are not widely used in online optimization. In this work, we demonstrate the use of an approximate neural network surrogate model as a prior mean for Gaussian processes used in Bayesian optimization in a realistic setting. We show that the initial performance of Bayesian optimization is improved by using neural network surrogate models, even when surrogate models make erroneous predictions. Finally, we quantify requirements on surrogate prediction accuracy to achieve optimization performance when solving problems in high dimensional input spaces.
