{"title":"用深度神经网络建模电磁问题","authors":"F. Xu, Shilei Fu","doi":"10.1109/COMPEM.2018.8496532","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":221352,"journal":{"name":"2018 IEEE International Conference on Computational Electromagnetics (ICCEM)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Modeling EM Problem with Deep Neural Networks\",\"authors\":\"F. Xu, Shilei Fu\",\"doi\":\"10.1109/COMPEM.2018.8496532\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":221352,\"journal\":{\"name\":\"2018 IEEE International Conference on Computational Electromagnetics (ICCEM)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE International Conference on Computational Electromagnetics (ICCEM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COMPEM.2018.8496532\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Conference on Computational Electromagnetics (ICCEM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMPEM.2018.8496532","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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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