Data-driven, multi-moment fluid modeling of Landau damping

• DOI: 10.1016/j.cpc.2022.108538
• Publication Date: 2022-09-08T14:47:58Z
• Authors: Wenjie Cheng; Haiyang Fu; Liang Wang; Chuanfei Dong; Yaqiu Jin; Mingle Jiang; Jiayu Ma; Yilan Qin; Kexin Liu
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; FOS: Physical sciences; 01 natural sciences; Physics - Plasma Physics; Space Physics (physics.space-ph); Machine Learning (cs.LG); Plasma Physics (physics.plasm-ph); Physics - Space Physics; 0103 physical sciences; Astrophysics - Instrumentation and Methods for Astrophysics; Instrumentation and Methods for Astrophysics (astro-ph.IM)

10 pages, 8 figures. Computer Physics Communications, in press<br/>Deriving governing equations of complex physical systems based on first principles can be quite challenging when there are certain unknown terms and hidden physical mechanisms in the systems. In this work, we apply a deep learning architecture to learn fluid partial differential equations (PDEs) of a plasma system based on the data acquired from a fully kinetic model. The learned multi-moment fluid PDEs are demonstrated to incorporate kinetic effects such as Landau damping. Based on the learned fluid closure, the data-driven, multi-moment fluid modeling can well reproduce all the physical quantities derived from the fully kinetic model. The calculated damping rate of Landau damping is consistent with both the fully kinetic simulation and the linear theory. The data-driven fluid modeling of PDEs for complex physical systems may be applied to improve fluid closure and reduce the computational cost of multi-scale modeling of global systems.<br/>