基于物理的带电粒子加速器束流动力学深度神经网络
Physics-Based Deep Neural Networks for Beam Dynamics in Charged Particle Accelerators
摘要：
本文提出了一种新的方法来构建神经网络模型带电粒子束动力学。在我们的方法中，泰勒映射产生的动态表示映射到一个多项式神经网络的权重。由此产生的网络在训练之前以完美的精度近似动态系统，并提供了根据额外的实验数据调整网络权重的可能性。我们提出了一种辛正则化方法，这样的多项式神经网络，总是限制训练模型的哈密顿系统，并显着改善了训练过程。所提出的网络可用于束流动力学模拟或微调的光束光学模型与实验数据。该网络的结构允许对具有大量磁体的大型加速器进行建模。我们展示了我们的方法的例子，现有的PETRA III和计划的PETRA IV存储环在DESY。
英文摘要：
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.
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