用深度神经网络建模电磁问题

2018 IEEE International Conference on Computational Electromagnetics (ICCEM) Pub Date : 2018-03-01 DOI:10.1109/COMPEM.2018.8496532

F. Xu, Shilei Fu

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引用次数: 7

摘要

本文探讨了利用深度神经网络(DNN)对电磁正演问题进行建模的潜力。作为初步尝试，我们使用深度卷积神经网络(CNN)拟合二维有限元边界积分(FE-BI)模型计算的非均匀圆形区域的散射场。该方法提供了一种快速映射特定EM问题输入到输出的新工具，为DNN求解逆问题的进一步研究奠定了基础。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Modeling EM Problem with Deep Neural Networks

This paper investigates the potential of using deep neural network (DNN) to model electromagnetic forward problems. As a preliminary attempt, we use a deep convolutional neural network (CNN) to fit the scattered field of an inhomogeneous circular region as calculated by a 2D Finite Element-Boundary Integral (FE-BI) model. This approach provides a new tool to fast map input to output of a specific EM problem, which builds basis for further study on solving inverse problem with DNN.

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2018 IEEE International Conference on Computational Electromagnetics (ICCEM)

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