CLC number: O313
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-05-17
Cited: 0
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Citations: Bibtex RefMan EndNote GB/T7714
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.
@article{title="A deep energy method for functionally graded porous beams",
author="Arvin Mojahedin, Mohammad Salavati, Timon Rabczuk",
journal="Journal of Zhejiang University Science A",
volume="22",
number="6",
pages="492-498",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2000317"
}
%0 Journal Article
%T A deep energy method for functionally graded porous beams
%A Arvin Mojahedin
%A Mohammad Salavati
%A Timon Rabczuk
%J Journal of Zhejiang University SCIENCE A
%V 22
%N 6
%P 492-498
%@ 1673-565X
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2000317
TY - JOUR
T1 - A deep energy method for functionally graded porous beams
A1 - Arvin Mojahedin
A1 - Mohammad Salavati
A1 - Timon Rabczuk
J0 - Journal of Zhejiang University Science A
VL - 22
IS - 6
SP - 492
EP - 498
%@ 1673-565X
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
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.
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