利用先验均值模型加速粒子加速器的贝叶斯优化
Leveraging Prior Mean Models for Faster Bayesian Optimization of Particle Accelerators
AI总结：
本文提出将基于模拟或历史数据训练的快速神经网络作为高斯过程先验均值函数，以提升粒子加速器贝叶斯优化在高维及模型失配情形下的收敛速度，并在 LCLS 与 ATLAS 场景验证迁移应用。
摘要：
调谐粒子加速器是一项具有挑战性且耗时的任务，可以借助合适的优化算法（例如基于模型的贝叶斯优化技术）实现自动化并高效执行。贝叶斯算法的主要优势之一是能够将有关束流物理和历史行为的先验信息纳入用于做出控制决策的模型中。在本工作中，我们研究了如何将加速器物理先验信息纳入贝叶斯优化算法：利用在模拟或历史数据集上训练的、执行快速的神经网络模型，作为高斯过程模型中的先验均值函数。我们表明，在理想情况下，该技术显著提高了在高维调谐参数空间中收敛到最优解的速度。此外，我们证明，即使在非理想情况下，即束流动力学的先验模型与实验条件并不完全匹配时，使用该技术仍可提升收敛速度。最后，我们展示了如何利用这些方法改进实际应用中的优化，例如将从束流动力学模拟中获得的信息迁移到 LCLS 注入器的在线控制中，以及将实验测量中获得的知识在不同运行模式之间迁移，例如在 ATLAS 重离子加速器上加速不同离子种类。
英文摘要：
Tuning particle accelerators is a challenging and time-consuming task that can be automated and carried out efficiently using suitable optimization algorithms, such as model-based Bayesian optimization techniques. One of the major advantages of Bayesian algorithms is the ability to incorporate prior information about beam physics and historical behavior into the model used to make control decisions. In this work, we examine incorporating prior accelerator physics information into Bayesian optimization algorithms by utilizing fast executing, neural network models trained on simulated or historical datasets as prior mean functions in Gaussian process models. We show that in ideal cases, this technique substantially increases convergence speed to optimal solutions in high-dimensional tuning parameter spaces. Additionally, we demonstrate that even in non-ideal cases, where prior models of beam dynamics do not exactly match experimental conditions, the use of this technique can still enhance convergence speed. Finally, we demonstrate how these methods can be used to improve optimization in practical applications, such as transferring information gained from beam dynamics simulations to online control of the LCLS injector, and transferring knowledge gained from experimental measurements across different operating modes, such as accelerating different ion species at the ATLAS heavy ion accelerator.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
