基于深度学习架构的微波等离子体相互作用二维仿真方法
Deep Learning Architecture Based Approach For 2D-Simulation of Microwave Plasma Interaction
摘要：
本文提出了一种基于卷积神经网络（CNN）的深度学习模型，该模型的灵感来自UNet，具有一系列具有跳跃连接的编码器和解码器单元，用于模拟微波-等离子体相互作用。微波在复杂等离子体介质中的传输、吸收和反射特性主要取决于电磁波频率与电子等离子体频率的比值以及等离子体的密度分布。本文研究了固定频率（1 GHz）和振幅的平面电磁波入射到具有不同高斯密度分布（在$1\times 10^{17}-1\times 10^{22}{m^{-3}}$范围内）的等离子体介质上的散射问题。与微波等离子体相互作用的训练数据已使用2D-FDTD（时域有限差分）为基础的模拟生成。然后，训练的深度学习模型用于再现1GHz入射微波在不同等离子体轮廓上的散射电场值，误差小于2%。我们提出了一个完整的基于深度学习（DL）的管道来训练，验证和评估模型。我们比较网络的结果，使用各种指标，如SSIM指数，平均百分比误差和均方误差，从完善的基于FDTD的EM求解器获得的物理数据。据我们所知，这是第一次努力探索DL为基础的方法模拟复杂的微波等离子体相互作用。与现有的计算技术相比，这项工作中提出的深度学习技术速度非常快，可以作为一种新的、前瞻性的和替代性的计算方法，用于在实时场景中研究微波-等离子体相互作用。
英文摘要：
This paper presents a convolutional neural network (CNN)-based deep learning model, inspired from UNet with series of encoder and decoder units with skip connections, for the simulation of microwave-plasma interaction. The microwave propagation characteristics in complex plasma medium pertaining to transmission, absorption and reflection primarily depends on the ratio of electromagnetic (EM) wave frequency and electron plasma frequency, and the plasma density profile. The scattering of a plane EM wave with fixed frequency (1 GHz) and amplitude incident on a plasma medium with different gaussian density profiles (in the range of $1\times 10^{17}-1\times 10^{22}{m^{-3}}$) have been considered. The training data associated with microwave-plasma interaction has been generated using 2D-FDTD (Finite Difference Time Domain) based simulations. The trained deep learning model is then used to reproduce the scattered electric field values for the 1GHz incident microwave on different plasma profiles with error margin of less than 2\%. We propose a complete deep learning (DL) based pipeline to train, validate and evaluate the model. We compare the results of the network, using various metrics like SSIM index, average percent error and mean square error, with the physical data obtained from well-established FDTD based EM solvers. To the best of our knowledge, this is the first effort towards exploring a DL based approach for the simulation of complex microwave plasma interaction. The deep learning technique proposed in this work is significantly fast as compared to the existing computational techniques, and can be used as a new, prospective and alternative computational approach for investigating microwave-plasma interaction in a real time scenario.
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