Detection and Prediction of Equilibrium States in Kinetic Plasma Simulations via Mode Tracking using Reduced-Order Dynamic Mode Decomposition

TL;DR

This paper introduces a reduced-order model using dynamic mode decomposition to efficiently detect and predict equilibrium states in kinetic plasma simulations, enhancing simulation speed and understanding of plasma behavior.

Contribution

The paper develops an in-line sliding-window DMD method for real-time detection of plasma equilibrium states from high-fidelity EMPIC data, improving upon existing techniques.

Findings

DMD accurately reconstructs electric field patterns from plasma data.

The method effectively identifies transitions from transient to equilibrium states.

Case studies demonstrate the approach's potential and limitations.

Abstract

A dynamic mode decomposition (DMD) based reduced-order model (ROM) is developed for tracking, detection, and prediction of kinetic plasma behavior. DMD is applied to the high-fidelity kinetic plasma model based on the electromagnetic particle-in-cell (EMPIC) algorithm to extract the underlying dynamics and key features of the model. In particular, the ability of DMD to reconstruct the spatial pattern of the self electric field from high-fidelity data and the effect of DMD extrapolated self-fields on charged particle dynamics are investigated. An in-line sliding-window DMD method is presented for identifying the transition from transient to equilibrium state based on the loci of DMD eigenvalues in the complex plane. The in-line detection of equilibrium state combined with time extrapolation ability of DMD has the potential to effectively expedite the simulation. Case studies involving electron beams and plasma ball are presented to assess the strengths and limitations of the proposed method.