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Updated: Aug 13, 2025

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

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Using kernel-based statistical distance to study the dynamics of charged particle beams in particle-based simulation

Chad E Mitchell¹, Robert D Ryne¹, Kilean Hwang²

¹Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA.

Physical Review. E

|January 21, 2023

Summary

Statistical distance measures, like maximum mean discrepancy, offer new numerical diagnostics for charged-particle beam simulations. These methods enhance the characterization of complex beam dynamics in nonlinear and high-intensity systems.

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Area of Science:

Physics
Computer Science
Data Science

Background:

Statistical distance measures are crucial in AI and machine learning.
Characterizing complex dynamics in charged-particle beams is challenging.
Existing methods for analyzing beam dynamics have limitations.

Purpose of the Study:

To adapt statistical distance measures for numerical diagnostics in charged-particle beam simulations.
To introduce kernel-based methods, such as maximum mean discrepancy, for beam analysis.
To provide sensitive characterization of dynamical processes in nonlinear or high-intensity beam systems.

Main Methods:

Implementation of statistical distance measures as numerical diagnostics.
Application of kernel-based methods, focusing on maximum mean discrepancy.
Utilizing measures of statistical dependence for beam analysis.

Main Results:

Developed sensitive diagnostics for charged-particle beam simulations.
Demonstrated effective characterization of dynamical processes in nonlinear/high-intensity beams.
Validated methods through benchmark problems and intense beam examples.

Conclusions:

Statistical distance measures offer powerful, computationally feasible diagnostics for charged-particle beams.
These methods can reveal otherwise difficult-to-characterize dynamical processes.
The approach is applicable to other many-body systems like plasmas and gravitational systems.