DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jung, Jaeho | ko |
dc.contributor.author | Yoon, Kyungho | ko |
dc.contributor.author | Lee, Phill-Seung | ko |
dc.date.accessioned | 2020-12-29T03:10:04Z | - |
dc.date.available | 2020-12-29T03:10:04Z | - |
dc.date.created | 2020-11-29 | - |
dc.date.issued | 2020-12 | - |
dc.identifier.citation | COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, v.372 | - |
dc.identifier.issn | 0045-7825 | - |
dc.identifier.uri | http://hdl.handle.net/10203/279221 | - |
dc.description.abstract | 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. | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCIENCE SA | - |
dc.title | Deep learned finite elements | - |
dc.type | Article | - |
dc.identifier.wosid | 000592535100010 | - |
dc.identifier.scopusid | 2-s2.0-85090402886 | - |
dc.type.rims | ART | - |
dc.citation.volume | 372 | - |
dc.citation.publicationname | COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING | - |
dc.identifier.doi | 10.1016/j.cma.2020.113401 | - |
dc.contributor.localauthor | Lee, Phill-Seung | - |
dc.contributor.nonIdAuthor | Yoon, Kyungho | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Finite element | - |
dc.subject.keywordAuthor | Solid element | - |
dc.subject.keywordAuthor | Stiffness matrix | - |
dc.subject.keywordAuthor | Artificial intelligence | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Neural network | - |
dc.subject.keywordPlus | NEURAL-NETWORKS | - |
dc.subject.keywordPlus | MITC3+SHELL ELEMENT | - |
dc.subject.keywordPlus | PERFORMANCE | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | GAME | - |
dc.subject.keywordPlus | GO | - |
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