Investigating deep energy method applications in thermoelasticity-Reference-Cited by-同舟云学术

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Investigating deep energy method applications in thermoelasticity

Published:2024-02 Issue: Volume:159 Page:302-314
ISSN:0955-7997
Container-title:Engineering Analysis with Boundary Elements
language:en
Short-container-title:Engineering Analysis with Boundary Elements

Author:

Lin Kuan-Chung^ORCID,Wang Kuo-Chou,Hu Cheng-Hung

Funder

National Cheng Kung University Hospital

Ministry of Education

National Cheng Kung University

National Science and Technology Council

Ministry of Science and Technology, Taiwan

Publisher

Elsevier BV

Subject

Applied Mathematics,Computational Mathematics,General Engineering,Analysis

Reference26 articles.

1. On the approximate realization of continuous mappings by neural networks;Funahashi;Neural Netw,1989

2. Deep vs. shallow networks: an approximation theory perspective;Mhaskar;Anal. Appl.,2016

3. Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations;Han;Commun Math Stat,2017

4. The deep ritz method: a deep learning-based numerical algorithm for solving variational problems;Yu;Commun Math Stat,2018

5. Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations;Beck;J Nonlinear Sci,2019

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. On the Gauss–Legendre quadrature rule of deep energy method for one-dimensional problems in solid mechanics;Finite Elements in Analysis and Design;2024-12

2. Interpretable physics-encoded finite element network to handle concentration features and multi-material heterogeneity in hyperelasticity;Computer Methods in Applied Mechanics and Engineering;2024-11

3. Variational temporal convolutional networks for I-FENN thermoelasticity;Computer Methods in Applied Mechanics and Engineering;2024-09

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