论文标题
基于物理的深神网络，用于带电粒子加速器中的光束动力学
Physics-Based Deep Neural Networks for Beam Dynamics in Charged Particle Accelerators
论文作者
论文摘要
本文提出了一种新的方法，用于构建对带电粒子束动力学的模拟神经网络。在我们的方法中，在动力学的表示中产生的泰勒图被映射到多项式神经网络的重量上。最终的网络在训练之前以完美的精度近似动态系统，并提供了在其他实验数据上调整网络权重的可能性。我们为此类多项式神经网络提出了一种符号正则化方法，该方法总是将训练的模型限制在汉密尔顿系统中，并显着改善训练程序。所提出的网络可用于光束动力学模拟或使用实验数据的光束光学模型进行微调。网络的结构允许对大量磁体进行大型加速器进行建模。我们展示了我们在现有的佩特拉三世（Petra III）和计划的佩特拉四世（Petra IV）存储环的示例中的方法。
This paper presents a novel approach for constructing neural networks which model charged particle beam dynamics. In our approach, the Taylor maps arising in the representation of dynamics are mapped onto the weights of a polynomial neural network. The resulting network approximates the dynamical system with perfect accuracy prior to training and provides a possibility to tune the network weights on additional experimental data. We propose a symplectic regularization approach for such polynomial neural networks that always restricts the trained model to Hamiltonian systems and significantly improves the training procedure. The proposed networks can be used for beam dynamics simulations or for fine-tuning of beam optics models with experimental data. The structure of the network allows for the modeling of large accelerators with a large number of magnets. We demonstrate our approach on the examples of the existing PETRA III and the planned PETRA IV storage rings at DESY.
