A new modified deep learning technique based on physics-informed neural networks (PINNs) for the shock-induced coupled thermoelasticity analysis in a porous material
{"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":"9 1","pages":""},"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}
引用次数: 0
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...
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.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
