{"title":"基于物理信息神经网络（PINNs）的新型修正深度学习技术，用于多孔材料的冲击诱导耦合热弹性分析","authors":"Katayoun Eshkofti, Seyed Mahmoud Hosseini","doi":"10.1080/01495739.2024.2321205","DOIUrl":null,"url":null,"abstract":"In this article, a new modified deep learning (DL) method based on physics-informed neural networks (PINNs) is proposed for analyzing generalized coupled thermoelasticity in a porous material under...","PeriodicalId":54759,"journal":{"name":"Journal of Thermal Stresses","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A new modified deep learning technique based on physics-informed neural networks (PINNs) for the shock-induced coupled thermoelasticity analysis in a porous material\",\"authors\":\"Katayoun Eshkofti, Seyed Mahmoud Hosseini\",\"doi\":\"10.1080/01495739.2024.2321205\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this article, a new modified deep learning (DL) method based on physics-informed neural networks (PINNs) is proposed for analyzing generalized coupled thermoelasticity in a porous material under...\",\"PeriodicalId\":54759,\"journal\":{\"name\":\"Journal of Thermal Stresses\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-04-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Thermal Stresses\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/01495739.2024.2321205\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MECHANICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Thermal Stresses","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/01495739.2024.2321205","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MECHANICS","Score":null,"Total":0}
A new modified deep learning technique based on physics-informed neural networks (PINNs) for the shock-induced coupled thermoelasticity analysis in a porous material
In this article, a new modified deep learning (DL) method based on physics-informed neural networks (PINNs) is proposed for analyzing generalized coupled thermoelasticity in a porous material under...
期刊介绍：
The first international journal devoted exclusively to the subject, Journal of Thermal Stresses publishes refereed articles on the theoretical and industrial applications of thermal stresses. Intended as a forum for those engaged in analytic as well as experimental research, this monthly journal includes papers on mathematical and practical applications. Emphasis is placed on new developments in thermoelasticity, thermoplasticity, and theory and applications of thermal stresses. Papers on experimental methods and on numerical methods, including finite element methods, are also published.
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