# Data-driven construction of a generalized kinetic collision operator from molecular dynamics

arXiv: https://arxiv.org/abs/2503.24208 · HTML: https://mlsanity.com/paper/2503.24208/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 8 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Built on by (most cited first, 3 of 3)

- [Machine learning moment closure models for the radiative transfer equation IV: enforcing symmetrizable hyperbolicity in two dimensions](https://mlsanity.com/paper/2604.20143/) (2026)
- [Fast spectral separation method for kinetic equation with anisotropic non-stationary collision operator retaining micro-model fidelity](https://mlsanity.com/paper/2510.15093/) (2025)
- [Learning collision operators from plasma phase space data using differentiable simulators](https://mlsanity.com/paper/2601.10885/) (2026)

## Abstract (arXiv)

We introduce a data-driven approach to learn a generalized kinetic collision operator directly from molecular dynamics. Unlike the conventional (e.g., Landau) models, the present operator takes an anisotropic form that accounts for a second energy transfer arising from the collective interactions between the pair of collision particles and the environment. Numerical results show that preserving the broadly overlooked anisotropic nature of the collision energy transfer is crucial for predicting the plasma kinetics with non-negligible correlations, where the Landau model shows limitations.