从分子动力学数据学习各向异性碰撞算子，提升等离子体非关联动力学预测精度。
Data-driven construction of a generalized kinetic collision operator from molecular dynamics
- 基于分子动力学数据直接学习各向异性碰撞算子
- 保留环境集体相互作用导致的二次能量传递机制
- 适用于具有显著相关性的等离子体系统，优于传统朗道模型
我们提出一种数据驱动方法，直接从分子动力学中学习广义动能碰撞算子。与传统（如朗道）模型不同，该算子具有各向异性形式，可捕捉碰撞粒子对与环境之间集体相互作用引发的二次能量转移。数值结果表明，在存在不可忽略相关性的等离子体动力学预测中，保持碰撞能量转移的各向异性特性至关重要，而朗道模型在此类情形下表现出局限性。
原文摘要 · Abstract (English)
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.
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