Structural Engineering and Mechanics

  • 제83권2호
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  • Pages.273-282
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  • 2022
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

A posteriori error estimation via mode-based finite element formulation using deep learning

  • Jung, Jaeho (Korea Atomic Energy Research Institute) ;
  • Park, Seunghwan (Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology) ;
  • Lee, Chaemin (Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology)
  • 투고 : 2022.04.12
  • 심사 : 2022.05.31
  • 발행 : 2022.07.25
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, we propose a new concept for error estimation in finite element solutions, which we call mode-based error estimation. The proposed error estimation predicts a posteriori error calculated by the difference between the direct finite element (FE) approximation and the recovered FE approximation. The mode-based FE formulation for the recently developed self-updated finite element is employed to calculate the recovered solution. The formulation is constructed by searching for optimal bending directions for each element, and deep learning is adopted to help find the optimal bending directions. Through various numerical examples using four-node quadrilateral finite elements, we demonstrate the improved predictive capability of the proposed error estimator compared with other competitive methods.

키워드

과제정보

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 resources from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20184030202000). This research was supported by a GPU server support program supervised by the NIPA (National IT Industry Promotion Agency).

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