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

Cheng, Wenjie; Fu, Haiyang; Wang, Liang; Dong, Chuanfei; Jin, Yaqiu; Jiang, Mingle; Ma, Jiayu; Qin, Yilan; Liu, Kexin

doi:10.1016/j.cpc.2022.108538

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

Journal Article · Thu Sep 08 00:00:00 EDT 2022 · Computer Physics Communications

DOI:https://doi.org/10.1016/j.cpc.2022.108538· OSTI ID:1914083

^[1];

^[2];

^[2]; Jin, Yaqiu ^[1]; Jiang, Mingle ^[1]; Ma, Jiayu ^[1]; Qin, Yilan ^[1]; Liu, Kexin ^[1]

Fudan Univ., Shanghai (China)
Princeton Univ., NJ (United States); Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)

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. Here, the learned multi-moment fluid PDEs are demonstrated to incorporate kinetic effect 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 the fluid closure and reduce the computational cost of multi-scale modeling of global systems.

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Research Organization:: Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)

Sponsoring Organization:: USDOE; National Key Research and Development Program of China; National Natural Science Foundation of China (NSFC)

Grant/Contract Number:: AC02-09CH11466; 2021YFA0717300; 42074189

OSTI ID:: 1914083

Alternate ID(s):: OSTI ID: 1960584

Journal Information:: Computer Physics Communications, Vol. 282; ISSN 0010-4655

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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71 CLASSICAL AND QUANTUM MECHANICS
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Machine learning
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