Data-driven, multi-moment fluid modeling of Landau damping
This work addresses the problem of modeling complex plasma systems for physicists and engineers by improving fluid closure and reducing computational costs, though it is incremental as it applies existing deep learning methods to a specific domain.
The researchers tackled the challenge of deriving governing equations for complex physical systems like plasmas by using a deep learning architecture to learn fluid PDEs from kinetic model data, successfully reproducing physical quantities and matching Landau damping rates with kinetic simulations and linear theory.
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.