标题： 用机器学习生成的初始条件加速动力学等离子体模拟 显示英文标题

标题： Accelerating kinetic plasma simulations with machine learning generated initial conditions

摘要： 计算机辅助工程多时间尺度等离子体系统，这些系统表现出准稳态解，由于需要大量时间步才能达到收敛，因此具有挑战性。 结合机器学习技术与传统第一性原理模拟和高性能计算，为解决这一挑战提供了许多有趣的途径。 我们考虑通过机器学习生成的初始条件加速粒子等离子体模拟。 该方法通过建模与微电子工业相关的电容耦合等离子体放电进行了演示。 三个模型在设备驱动频率和工作压力的参数空间上的模拟数据上进行训练。 这些模型结合了多层感知器、主成分分析和卷积神经网络的元素，以预测最终的时间平均离子密度和速度分布函数的轮廓。 这些数据驱动的初始条件生成器（ICGs）在使用离线程序测量时，将收敛时间平均加快了17.1倍，或在使用在线程序时加快了4.4倍，其中卷积神经网络表现最佳。 本文还概述了一个连续数据驱动模型改进和模拟加速的工作流程，旨在生成足够的数据以实现完整的器件数字孪生。 显示英文摘要

摘要： Computer aided engineering of multi-time-scale plasma systems which exhibit a quasi-steady state solution are challenging due to the large number of time steps required to reach convergence. Machine learning techniques combined with traditional first-principles simulations and high-performance computing offer many interesting pathways towards resolving this challenge. We consider acceleration of kinetic plasma simulations via machine learning generated initial conditions. The approach is demonstrated through modeling of capacitively coupled plasma discharges relevant to the microelectronics industry. Three models are trained on simulations across a parameter space of device driving frequency and operating pressure. The models incorporate elements of a multi-layer perceptron, principal component analysis, and convolutional neural networks to predict the final time-averaged profiles of ion-density and velocity distribution functions. These data-driven initial condition generators (ICGs) provide a mean speedup of 17.1x in convergence time, when measured using an offline procedure, or a 4.4x speedup with an online procedure, with convolutional neural networks leading to the best performance. The paper also outlines a workflow for continuous data-driven model improvement and simulation speedup, with the aim of generating sufficient data for full device digital twins.