Deep learned finite elements

Jaeho Jung, Kyungho Yoon, Phill Seung Lee

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Article number113401
JournalComputer Methods in Applied Mechanics and Engineering
Volume372
DOIs
Publication statusPublished - 2020 Dec 1

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2018R1A2B3005328 ). This work was also supported by the “Human Resources Program in Energy Technology” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20184030202000 ). This research was the result of a study on the “HPC Support” Project, supported by the Ministry of Science and ICT (MSIT) and National IT Industry Promotion Agency (NIPA) . We also thank Dr. Yonggyun Yu at Korea Atomic Energy Research Institute (KAERI) for his valuable comments.

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2018R1A2B3005328). This work was also supported by the ?Human Resources Program in Energy Technology? of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20184030202000). This research was the result of a study on the ?HPC Support? Project, supported by the Ministry of Science and ICT (MSIT) andNational IT Industry Promotion Agency (NIPA). We also thank Dr. Yonggyun Yu at Korea Atomic Energy Research Institute (KAERI) for his valuable comments.

Publisher Copyright:
© 2020 Elsevier B.V.

All Science Journal Classification (ASJC) codes

  • Computational Mechanics
  • Mechanics of Materials
  • Mechanical Engineering
  • Physics and Astronomy(all)
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'Deep learned finite elements'. Together they form a unique fingerprint.

Cite this