论文标题
使用基于内核的统计距离来研究基于粒子的仿真代码中带电粒子梁的动力学
Using Kernel-Based Statistical Distance to Study the Dynamics of Charged Particle Beams in Particle-Based Simulation Codes
论文作者
论文摘要
概率分布（统计距离）之间差异的度量广泛用于人工智能和机器学习领域。我们描述了如何将统计距离的某些度量作为涉及带电粒子光束的模拟的数值诊断。还描述了统计依赖性的相关度量。最终的诊断提供了对非线性或高强度系统中梁重要的动力学过程的敏感度量，否则这些过程很难表征。重点是基于内核的方法，例如最大平均差异，这些方法具有良好的数学基础和合理的计算复杂性。讨论了几个涉及强烈光束的基准问题和示例。虽然重点放在带电的粒子束上，但这些方法也可以应用于其他多体系统，例如等离子体或重力系统。
Measures of discrepancy between probability distributions (statistical distance) are widely used in the fields of artificial intelligence and machine learning. We describe how certain measures of statistical distance can be implemented as numerical diagnostics for simulations involving charged-particle beams. Related measures of statistical dependence are also described. The resulting diagnostics provide sensitive measures of dynamical processes important for beams in nonlinear or high-intensity systems, which are otherwise difficult to characterize. The focus is on kernel-based methods such as Maximum Mean Discrepancy, which have a well-developed mathematical foundation and reasonable computational complexity. Several benchmark problems and examples involving intense beams are discussed. While the focus is on charged-particle beams, these methods may also be applied to other many-body systems such as plasmas or gravitational systems.
