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CLC number: O313

On-line Access: 2021-06-21

Received: 2020-07-18

Revision Accepted: 2020-08-04

Crosschecked: 2021-05-17

Cited: 0

Clicked: 2556

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Timon Rabczuk

https://orcid.org/0000-0002-7150-296X

Arvin Mojahedin

https://orcid.org/0000-0002-2333-5984

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Journal of Zhejiang University SCIENCE A 2021 Vol.22 No.6 P.492-498

http://doi.org/10.1631/jzus.A2000317


A deep energy method for functionally graded porous beams


Author(s):  Arvin Mojahedin, Mohammad Salavati, Timon Rabczuk

Affiliation(s):  Institute of Structural Mechanics, Bauhaus-Universität Weimar, Weimar 99423, Germany; more

Corresponding email(s):   timon.rabczuk@tdtu.edu.vn

Key Words:  Energy-based method, Multilayer perceptron methodology, Functionally graded porous materials, Euler-Bernoulli beam theory


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Arvin Mojahedin, Mohammad Salavati, Timon Rabczuk. A deep energy method for functionally graded porous beams[J]. Journal of Zhejiang University Science A, 2021, 22(6): 492-498.

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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2000317"
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T1 - A deep energy method for functionally graded porous beams
A1 - Arvin Mojahedin
A1 - Mohammad Salavati
A1 - Timon Rabczuk
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DOI - 10.1631/jzus.A2000317


Abstract: 
We present a deep energy method (DEM) to solve functionally graded porous beams. We use the Euler-Bernoulli assumptions with varying mechanical properties across the thickness. DEM is subsequently developed, and its performance is demonstrated by comparing the analytical solution, which was adopted from our previous work. The proposed method completely eliminates the need of a discretization technique, such as the finite element method, and optimizes the potential energy of the beam to train the neural network. Once the neural network has been trained, the solution is obtained in a very short amount of time.

一种功能梯度多孔梁的深度能量方法

概要:本文提出了一种深度能量方法(DEM)来求解功能梯度多孔梁.采用欧拉-伯努利假设,且功能梯度多孔梁在整个厚度范围内具有不同的力学性能.随后开发了DEM,并通过与作者在以前的工作中采用的解析解进行对比证明了其性能.本文所提出的方法完全不需要离散化技术(例如有限元方法),而是通过优化梁的势能来训练神经网络. 一旦神经网络训练好,其求解可在很短的时间内完成.
关键词:能量法;多层感知器方法;梯度功能多孔材料;Euler-Bernoulli梁理论

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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