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

Abstract

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.

arXiv comment10 pages, 8 figures. Computer Physics Communications, in press

References in corpus (13)

• Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
• Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
• PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network
• Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations
• PDE-Net: Learning PDEs from Data
• Discontinuous Galerkin algorithms for fully kinetic plasmas
• Uniformly Accurate Machine Learning Based Hydrodynamic Models for Kinetic Equations
• AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
• Global Ten-Moment Multifluid Simulations of the Solar Wind Interaction with Mercury: From the Planetary Conducting Core to the Dynamic Magnetosphere
• Electron Physics in 3D Two-Fluid Ten-Moment Modeling of Ganymede's Magnetosphere
• Exact and Locally Implicit Source Term Solvers for Multifluid-Maxwell Systems
• Neural network representability of fully ionized plasma fluid model closures
• Data-driven Discovery of Partial Differential Equations for Multiple-Physics Electromagnetic Problem

Cited by in corpus (9)

• Data-driven modeling of Landau damping by physics-informed neural networks
• Machine-learning heat flux closure for multi-moment fluid modeling of nonlinear Landau damping
• Numerical Study of Magnetic Island Coalescence Using Magnetohydrodynamics With Adaptively Embedded Particle-In-Cell Model
• Data-driven discovery of a heat flux closure for electrostatic plasma phenomena
• Electron cyclotron drift instability and anomalous transport: two-fluid moment theory and modeling
• Machine-learning Closure for Vlasov-Poisson Dynamics in Fourier-Hermite Space
• Surrogate Modeling of Landau Damping with Deep Operator Networks
• Optimal Landau-type closure parameters for two-fluid simulations of plasma turbulence at kinetic scales
• The Machine Learning Approach to Moment Closure Relations for Plasma: A Review