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.

开发了一种基于动态模式分解 (DMD) 的降阶模型 (ROM)，用于跟踪、检测和预测等离子体动力学行为。DMD应用于基于EMPIC算法的高保真动力学等离子体模型，以提取模型的底层动力学和关键特征。特别是，研究了 DMD 从高保真数据重建自电场空间模式的能力，以及 DMD 外推自场对带电粒子动力学的影响。提出了一种基于 DMD 特征值在复平面中的轨迹的在线滑动窗口 DMD 方法，用于识别从瞬态到平衡状态的转变。平衡状态的在线检测与 DMD 的时间外推能力相结合，有可能有效加快模拟速度。介绍了涉及电子束和等离子球的案例研究，以评估所提出方法的优势和局限性。