Deep learned finite elements

Cited 33 time in webofscience Cited 12 time in scopus
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In this paper, we propose a method that employs deep learning, an artificial intelligence technique, to generate stiffness matrices of finite elements. The proposed method is used to develop 4- and 8-node 2D solid finite elements. The deep learned finite elements practically pass the patch tests and the zero energy mode tests. Through various numerical examples, the performance of the developed elements is investigated and compared with those of existing elements. Computation efficiency is also studied. It was confirmed that the deep learned finite elements can potentially outperform existing finite elements. The proposed method can be applied to generate various types of finite elements in the future.
Publisher
ELSEVIER SCIENCE SA
Issue Date
2020-12
Language
English
Article Type
Article
Citation

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, v.372

ISSN
0045-7825
DOI
10.1016/j.cma.2020.113401
URI
http://hdl.handle.net/10203/279221
Appears in Collection
ME-Journal Papers(저널논문)
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