论文标题
通过模式跟踪使用降低阶动力模式分解，在动力学等离子体模拟中检测和预测平衡状态
Detection and Prediction of Equilibrium States in Kinetic Plasma Simulations via Mode Tracking using Reduced-Order Dynamic Mode Decomposition
论文作者
论文摘要
开发了基于动态模式分解（DMD）的还原阶模型（ROM），用于跟踪，检测和预测动力学的血浆行为。 DMD应用于基于电磁粒子中（EMPIC）算法的高保真动力学等离子体模型，以提取模型的基本动力学和关键特征。特别是，研究了DMD从高保真数据中重建自我场的空间模式的能力以及DMD外推为电场对带电粒子动力学的影响。提出了一种在线滑动窗口DMD方法，用于根据复杂平面中DMD特征值的基因座识别从瞬态到平衡状态的过渡。平衡状态的在线检测与DMD的时间外推能力相结合具有有效加快模拟的潜力。提出了涉及电子束和血浆球的案例研究，以评估所提出方法的优势和局限性。
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.
