论文标题
不确定性意识到粒子加速器的基于ML的替代模型：Fermilab增强加速器复合物的研究
Uncertainty Aware ML-based surrogate models for particle accelerators: A Study at the Fermilab Booster Accelerator Complex
论文作者
论文摘要
标准的深度学习方法，例如集成模型，贝叶斯神经网络和分数回归模型，为数据驱动的深度学习模型提供了预测不确定性的估计。但是，由于他们的记忆力，推理成本以及正确捕获分布不确定性的能力，它们的应用可能会受到限制。此外，其中一些模型需要训练后校准，这限制了其用于连续学习应用的能力。在本文中，我们提出了一种新的方法，可以通过校准的不确定性提供预测，其中包括分布式贡献，并将其与费米国家加速器实验室（FNAL）增强加速器综合体的标准方法进行比较。
Standard deep learning methods, such as Ensemble Models, Bayesian Neural Networks and Quantile Regression Models provide estimates to prediction uncertainties for data-driven deep learning models. However, they can be limited in their applications due to their heavy memory, inference cost, and ability to properly capture out-of-distribution uncertainties. Additionally, some of these models require post-training calibration which limits their ability to be used for continuous learning applications. In this paper, we present a new approach to provide prediction with calibrated uncertainties that includes out-of-distribution contributions and compare it to standard methods on the Fermi National Accelerator Laboratory (FNAL) Booster accelerator complex.
