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arXiv 2209.04726physics.plasm-phastro-ph.IMcs.LGphysics.space-ph
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Landau阻尼的数据驱动、多力矩流体建模

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

Fudan University(复旦大学);Princeton University(普林斯顿大学);Princeton Plasma Physics Laboratory(普林斯顿等离子体物理实验室)

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

摘要:

当系统中存在某些未知项和隐藏的物理机制时,基于第一性原理推导复杂物理系统的控制方程是相当具有挑战性的。在这项工作中,我们应用深度学习架构来学习基于从完全动力学模型获得的数据的等离子体系统的流体偏微分方程(PDE)。学习的多力矩流体偏微分方程被证明将动力学效应,如朗道阻尼。基于学习的流体封闭性,数据驱动的多矩流体建模可以很好地再现从完全动力学模型导出的所有物理量。计算得到的Landau阻尼率与全动力学模拟和线性理论计算结果一致。数据驱动的复杂物理系统的偏微分方程流体建模可以应用于提高流体封闭性和降低全球系统多尺度建模的计算成本。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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