Full Text:   <2315>

Summary:  <1815>

CLC number: O313

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2021-05-17

Cited: 0

Clicked: 4316

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-Universit�t 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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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.

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