We present a generalized, data-driven collisional operator for one-component plasmas, learned from molecular dynamics simulations, to extend the collisional kinetic model beyond the weakly coupled regime. The proposed operator features an anisotropic, non-stationary collision kernel that accounts for particle correlations typically neglected in classical Landau formulations. To enable efficient numerical evaluation, we develop a fast spectral separation method that represents the kernel as a low-rank tensor product of univariate basis functions. This formulation admits an $O(N \log N)$ algorithm via fast Fourier transforms and preserves key physical properties, including discrete conservation laws and the H-theorem, through a structure-preserving central difference discretization. Numerical experiments demonstrate that the proposed model accurately captures plasma dynamics in the moderately coupled regime beyond the standard Landau model while maintaining high computational efficiency and structure-preserving properties.
翻译:本文提出了一种基于分子动力学模拟学习的广义数据驱动碰撞算子,用于单组分等离子体,以将碰撞动理学模型扩展至弱耦合区域之外。所提出的算子具有各向异性、非定常的碰撞核,该碰撞核考虑了经典朗道表述中通常忽略的粒子关联效应。为实现高效的数值计算,我们发展了一种快速谱分离方法,将碰撞核表示为单变量基函数的低秩张量积。该表述通过快速傅里叶变换实现了一种$O(N \log N)$算法,并通过结构保持的中心差分离散化,保留了包括离散守恒律和H定理在内的关键物理性质。数值实验表明,所提出的模型在超越标准朗道模型的中等耦合区域中,能够精确捕捉等离子体动力学,同时保持高计算效率和结构保持特性。