A Deep Energy Method for the Analysis of Thermoporoelastic Functionally Graded Beams-Reference-Cited by-同舟云学术

A Deep Energy Method for the Analysis of Thermoporoelastic Functionally Graded Beams

Published:2022-09-21 Issue:08 Volume:19 Page:
ISSN:0219-8762
Container-title:International Journal of Computational Methods
language:en
Short-container-title:Int. J. Comput. Methods

Author:

Mojahedin Arvin¹,Hashemitaheri Maryam²,Salavati Mohammad³,Fu Xiaolong⁴,Rabczuk Timon⁵

Affiliation:

1. Institute of Structural Mechanics, Bauhaus-Universität Weimar, Marienstrasse 7, Weimar 99423, Germany

2. Department of Mechanical Engineering and Engineering Science, UNC Charlotte, Charlotte, NC, USA

3. Department of Materials Engineering, Institute of Material Science and Technology, Technical University of Berlin, Berlin 10623, Germany

4. Xi’an Modern Chemistry Research Institute, Xi’an 710065, P. R. China

5. Institute of Structure Mechanics, Bauhaus University Weimar, Weimar 99423, Germany

Abstract

In this paper, we present a deep energy method for functionally graded beams based on both Euler–Bernoulli and Timoshenko beam theory to study their mechanical and thermal properties. We consider the effect of temperature as well as porosity under saturated and unsaturated conditions. The objective function is related to the total potential energy (and boundary conditions) and minimized through neural network training. The results are validated by comparison with benchmark problems available in the literature.

Publisher

World Scientific Pub Co Pte Ltd

Subject

Computational Mathematics,Computer Science (miscellaneous)

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0219876221430209

Reference27 articles.

1. Artificial Neural Network Methods for the Solution of Second Order Boundary Value Problems

2. Thermal buckling and postbuckling responses of geometrically imperfect FG porous beams based on physical neutral plane

3. Limit load analysis and imperfection sensitivity of heated or compressed FGM beams on nonlinear softening elastic foundation

4. Theory of Buckling of a Porous Slab and Its Thermoelastic Analogy

5. Machine and Deep Learning

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. Investigating deep energy method applications in thermoelasticity;Engineering Analysis with Boundary Elements;2024-02

2. Innovative deep energy method for piezoelectricity problems;Applied Mathematical Modelling;2024-02

3. An Introduction to Programming Physics-Informed Neural Network-Based Computational Solid Mechanics;International Journal of Computational Methods;2023-06-08