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

Abstract

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

6 figures

References in corpus (10)

• Magnetic reconnection in the era of exascale computing and multiscale experiments
• Reconnection-Driven Energy Cascade in Magnetohydrodynamic Turbulence
• Images of magnetospheric reconnection-powered radiation around supermassive black holes
• A Six-moment Multi-fluid Plasma Model
• Data-driven, multi-moment fluid modeling of Landau damping
• Data-driven modeling of Landau damping by physics-informed neural networks
• In Search of a Data Driven Symbolic Multi-Fluid 10-Moment Model Closure
• Identification of high order closure terms from fully kinetic simulations using machine learning
• Numerical Study of Magnetic Island Coalescence Using Magnetohydrodynamics With Adaptively Embedded Particle-In-Cell Model
• Discovery of double Hall pattern associated with collisionless magnetic reconnection in dusty plasmas

Cited by in corpus (7)

• Machine-learning Closure for Vlasov-Poisson Dynamics in Fourier-Hermite Space
• Electron neural closure for turbulent magnetosheath simulations: energy channels
• Surrogate Modeling of Landau Damping with Deep Operator Networks
• Embedding physical symmetries into machine-learned reduced plasma physics models via data augmentation
• Electron Heat Flux and Whistler Instability in the Earth's Magnetosheath
• 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