Machine-learning heat flux closure for multi-moment fluid modeling of nonlinear Landau damping

• DOI: 10.1073/pnas.2419073122
• Publication Date: 2025-03-12T17:39:51Z
• Authors: Ziyu Huang; Chuanfei Dong; Liang Wang
• Keywords: Moment closure; Closure (psychology); Landau damping

Nonlinear plasma physics problems are usually simulated through comprehensive modeling of phase space. The extreme computational cost such simulations has motivated the development multi-moment fluid models. However, a major challenge been finding suitable closure for these Recent developments in physics-informed machine learning have led to renewed interest constructing accurate terms. In this study, we take an approach that integrates kinetic from first-principles Vlasov into model (through heat flux term) using Fourier neural operator—a network architecture. Without resolving space dynamics, new is capable capturing nonlinear evolution Landau damping process exactly matches simulation results. This learning–assisted provides computationally affordable framework surpasses previous models accurately complex systems.