Surrogate Modeling of Landau Damping with Deep Operator Networks

TL;DR

This paper develops a surrogate model using Deep Operator Networks to efficiently simulate Landau damping in plasmas, enabling large-scale space and astrophysical plasma modeling with high accuracy.

Contribution

It introduces a novel application of DeepONets to model plasma dynamics based on kinetic simulation data, capturing complex behaviors efficiently.

Findings

DeepONets accurately reproduce electric field energy evolution.

Model performs well in both linear and nonlinear regimes.

Validated robustness across various plasma conditions.

Abstract

Kinetic simulations excel at capturing microscale plasma physics phenomena with high accuracy, but their computational demands make them impractical for modeling large-scale space and astrophysical systems. In this context, we build a surrogate model, using Deep Operator Networks (DeepONets), based upon the Vlasov-Poisson simulation data to model the dynamical evolution of plasmas, focusing on the Landau damping process - a fundamental kinetic phenomenon in space and astrophysical plasmas. The trained DeepONets are able to capture the evolution of electric field energy in both linear and nonlinear regimes under various conditions. Extensive validation highlights DeepONets' robust performance in reproducing complex plasma behaviors with high accuracy, paving the way for large-scale modeling of space and astrophysical plasmas.