A new modified deep learning technique based on physics-informed neural networks (PINNs) for the shock-induced coupled thermoelasticity analysis in a porous material

IF 2.6 3区工程技术 Q2 MECHANICS

Journal of Thermal Stresses Pub Date : 2024-04-10 DOI:10.1080/01495739.2024.2321205

Katayoun Eshkofti, Seyed Mahmoud Hosseini

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...

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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

本文提出了一种基于物理信息神经网络（PINNs）的改进型深度学习（DL）新方法，用于分析多孔材料在...

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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来源期刊

Journal of Thermal Stresses 工程技术-力学

CiteScore

5.20

自引率

7.10%

发文量

审稿时长

3 months

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.