{"title":"基于物理的时域电磁辐射问题深度学习","authors":"Yingze Ge, Liangshuai Guo, Maokun Li","doi":"10.1109/IMBioC52515.2022.9790302","DOIUrl":null,"url":null,"abstract":"We explore the application of physics-informed deep learning to solve time-domain electromagnetic problems. This method takes advantage of the differentiability of neural networks and fully integrated with first principles. Compared to traditional approach, there is no need of discretization. Numerical experiment verifies the accuracy of this scheme.","PeriodicalId":305829,"journal":{"name":"2022 IEEE MTT-S International Microwave Biomedical Conference (IMBioC)","volume":"64 2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Physics-Informed Deep Learning for Time-Domain Electromagnetic Radiation Problem\",\"authors\":\"Yingze Ge, Liangshuai Guo, Maokun Li\",\"doi\":\"10.1109/IMBioC52515.2022.9790302\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We explore the application of physics-informed deep learning to solve time-domain electromagnetic problems. This method takes advantage of the differentiability of neural networks and fully integrated with first principles. Compared to traditional approach, there is no need of discretization. Numerical experiment verifies the accuracy of this scheme.\",\"PeriodicalId\":305829,\"journal\":{\"name\":\"2022 IEEE MTT-S International Microwave Biomedical Conference (IMBioC)\",\"volume\":\"64 2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE MTT-S International Microwave Biomedical Conference (IMBioC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IMBioC52515.2022.9790302\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE MTT-S International Microwave Biomedical Conference (IMBioC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IMBioC52515.2022.9790302","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Physics-Informed Deep Learning for Time-Domain Electromagnetic Radiation Problem
We explore the application of physics-informed deep learning to solve time-domain electromagnetic problems. This method takes advantage of the differentiability of neural networks and fully integrated with first principles. Compared to traditional approach, there is no need of discretization. Numerical experiment verifies the accuracy of this scheme.
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