Work

Data-Driven Construction of a Generalized Kinetic Collision Operator from Molecular Dynamics

Year: 2025

Type: article

Abstract: We introduce a data-driven approach to learn generalized collision operators from molecular dynamics. Unlike conventional models (e.g., Landau), the present operator takes a symmetry-breaking form that depends not only on the relative velocity but also on the average velocity of the collision pair, capturing heterogeneous energy transfer arising from collective interactions with the environment. Numerical results show that preserving this overlooked anisotropy is crucial for predicting plasma kinetics and transport coefficients in the moderate coupling regime, where the Landau model fails.

Source: Physical Review Letters

DOI: 10.1103/gml7-f2ng

Authors: Yue Zhao, Joshua Burby, Andrew Christlieb, Huan Lei

Institutions: Michigan State University, The University of Texas at Austin

Language: English

Topic: Gas Dynamics and Kinetic Theory

Subfield: Applied Mathematics

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: Access Fund, National Science Foundation, U.S. Department of Energy

Awards: CAREER: Machine-Learning Construction of Energy-Stable Non-Newtonian Fluid Hydrodynamics with Molecular Fidelity, G5224991490, CENTER FOR HIERARCHICAL AND ROBUST MODELING OF NON-EQUILIBRIUM TRANSPORT (CHARMNET), ESTABLISHMENT OF AN INSTITUTE FOR FUSION STUDIES, Machine-Learning-Based Modeling of Multiscale Dynamic Systems with Non-Markovian State-Dependent Memory