电波科学学报2024，Vol.39Issue(3)：552-560,9.DOI:10.12265/j.cjors.2023217
基于JEC-FDTD等效循环神经网络的电磁建模和等离子体参数反演
Electromagnetic modeling and plasma parameters inversion based on JEC-FDTD equivalent recurrent neural network
摘要
Abstract
Electromagnetic wave propagation in magnetized plasma stands as a significant focal point in research.The effective formulation and precise resolution of equations to model electromagnetic plasma coupling within specific scenarios carry profound research significance and pose noteworthy challenges.These undertakings are pivotal in delving into the intricate mechanisms governing the nonlinear interaction between electromagnetic waves and plasma.This paper introduces a novel approach employing recurrent neural networks(RNNs)for both forward and backward modeling of electromagnetic plasma.The forward propagation process emulates the current density convolution finite difference time-domain method(JEC-FDTD),accommodating arbitrary magnetic tilts.Consequently,it not only resolves targeted electromagnetic modeling issues but also offers the advantage of efficient computation through parallel processing.By establishing a forward-differentiable simulation process,our methodology adeptly leverages automatic differentiation techniques to precisely compute gradients.This enables us to effectively address inverse problems through network training.As a result,our approach harnesses observed time-domain scattered field signals to successfully deduce pivotal plasma parameters.Taking advantage of the optimization strategy and dedicated hardware support provided by deep learning,the method can be applied to electromagnetic modeling and plasma parameter inversion in various simulation scenarios.关键词
电流密度卷积时域有限差分(JEC-FDTD)方法/磁化等离子体/循环神经网络(RNN)/物理启发的机器学习算法/参数反演Key words
JEC-FDTD/magnetized plasma/recurrent neural networks/physics-inspired machine learning algorithms/parameter inversion分类
天文与地球科学引用本文复制引用
覃一澜,马嘉禹,付海洋,徐丰..基于JEC-FDTD等效循环神经网络的电磁建模和等离子体参数反演[J].电波科学学报,2024,39(3):552-560,9.基金项目
国家自然科学基金(42074189) （42074189）
电波环境特性及模化技术重点实验室开放基金(202103013) （202103013）
