基于物理信息的深度学习方法的粒子追踪
Particle tracking with physics-informed deep learning methods
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中文总结 AI 辅助
针对传统粒子模拟计算成本高的问题，修改SympNet架构结合DeepONet实现快速粒子模拟，在圆形加速器玩具装置上验证了方法的有效性。
中文摘要 AI 辅助
模拟带电粒子在电磁场中的运动对粒子加速器的设计与优化至关重要。传统工具依赖辛积分方案，其精度高但计算成本高昂，因此在中高维参数空间的优化及数万至数百万粒子的模拟中，计算代价难以承受。本研究探索采用现代机器学习工具（尤其是SympNet与DeepONet）实现快速粒子模拟的可能性，核心创新在于对传统SympNet架构进行修改，以学习参数化哈密顿动力学。模型在包含两种不同类型、场强可变的四极磁铁的圆形加速器玩具装置上进行训练与测试。所有模型的推理速度均快于辛积分器，但精度显著降低；其中SympNet实现的均方误差最低。此外，还采用DeepONet预测粒子密度的演化，该演化源自单粒子模拟结果。
英文摘要
Simulating the motion of charged particles in electromagnetic fields is essential for designing and optimising particle accelerators. Conventional tools rely on symplectic integration schemes, which provide high accuracy but are computationally expensive. As a consequence, optimisation in moderate to high-dimensional parameter spaces as well as simulations of tens of thousands to millions of particles can be computationally prohibitive. This contribution explores the possibilities of employing modern machine-learning based tools, in particular SympNet and DeepONet, to enable fast particle simulations. A major novelty is the modification of the conventional SympNet architecture to enable learning of parametric Hamiltonian dynamics. The models are trained and tested on a toy setup of a circular accelerator comprising two different types of quadrupole magnets with varying field strengths. All models achieved faster inference than the symplectic integrator, at the expanse of significantly reduced accuracy. The SympNet implementation achieved the lowest mean squared error. Additionally, a DeepONet was employed to predict the evolution of particle densities, derived from the single-particle simulations.
