Accelerating Particle-in-Cell Kinetic Plasma Simulations via Reduced-Order Modeling of Space-Charge Dynamics using Dynamic Mode Decomposition

TL;DR

This paper introduces a data-driven reduced-order modeling approach using dynamic mode decomposition to accelerate and analyze electromagnetic particle-in-cell plasma simulations by capturing space-charge dynamics efficiently.

Contribution

The paper presents a novel application of DMD for reduced-order modeling of current density in EMPIC simulations, significantly speeding up computations and enabling new analysis capabilities.

Findings

DMD accurately models current density evolution in plasma simulations.

Reduced-order models achieve substantial speed-up in EMPIC simulations.

The approach is effective for various plasma phenomena like electron beams and oscillations.

Abstract

We present a data-driven reduced-order modeling of the space-charge dynamics for electromagnetic particle-in-cell (EMPIC) plasma simulations based on dynamic mode decomposition (DMD). The dynamics of the charged particles in kinetic plasma simulations such as EMPIC is manifested through the plasma current density defined along the edges of the spatial mesh. We showcase the efficacy of DMD in modeling the time evolution of current density through a low-dimensional feature space. Not only do such DMD-based predictive reduced-order models help accelerate EMPIC simulations, they also have the potential to facilitate investigative analysis and control applications. We demonstrate the proposed DMD-EMPIC scheme for reduced-order modeling of current density, and speed-up in EMPIC simulations involving electron beam under the influence of magnetic field, virtual cathode oscillations, and backward wave oscillator.