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P.S. Lee, S.H. Park, J.H. Jung , "The potential of deep learning in the finite element method", in B.H.V. Topping, P. Iványi, (Editors), "Proceedings of the Eleventh International Conference on Engineering Computational Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 2, Paper 1.4, 2022, doi:10.4203/ccc.2.1.4

Keywords: finite element method, deep learning, 2D solid element, deep learned finite elements, self-updated finite element, shear locking, reference data model, iterative solution procedure.

Abstract

The Finite Element Method (FEM) has been widely used to obtain reliable solutions in various engineering fields. It is necessary to promote a leap of FEM technology by incorporating emerging technologies into FEM. Deep learning, one of artificial intelligence technologies, may have the surprising potential to innovate FEM. In this presentation, we investigate potential of deep learning in FEM through our previous studies. We developed two types of finite elements called “deep learned finite elements (DLFE)” and “self-updated finite element (SUFE)”. We compare the performance of the proposed elements with those of other existing quadrilateral elements through numerical examples. The DLFE and SUFE produce improved accuracy and computational efficiency. Deep learning has the meaningful potential for finite element technology. In this study, the proposed methods were only applied to develop 2D quadrilateral solid finite elements. We will enlarge the methods to various finite elements such as triangle 2D solid, plate, and shell finite elements.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 2 PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON ENGINEERING COMPUTATIONAL TECHNOLOGY Edited by: B.H.V. Topping and P. Iványi Paper 1.4 The potential of deep learning in the finite element method P.S. Lee¹, S.H. Park¹ and J.H. Jung² ¹Department of Mechanical Engineering, KAIST, Republic of Korea ²Korea Atomic Energy Research Institute, KAERI, Republic of Korea doi:10.4203/ccc.2.1.4 Full Bibliographic Reference for this paper P.S. Lee, S.H. Park, J.H. Jung , "The potential of deep learning in the finite element method", in B.H.V. Topping, P. Iványi, (Editors), "Proceedings of the Eleventh International Conference on Engineering Computational Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 2, Paper 1.4, 2022, doi:10.4203/ccc.2.1.4 Keywords: finite element method, deep learning, 2D solid element, deep learned finite elements, self-updated finite element, shear locking, reference data model, iterative solution procedure. Abstract The Finite Element Method (FEM) has been widely used to obtain reliable solutions in various engineering fields. It is necessary to promote a leap of FEM technology by incorporating emerging technologies into FEM. Deep learning, one of artificial intelligence technologies, may have the surprising potential to innovate FEM. In this presentation, we investigate potential of deep learning in FEM through our previous studies. We developed two types of finite elements called “deep learned finite elements (DLFE)” and “self-updated finite element (SUFE)”. We compare the performance of the proposed elements with those of other existing quadrilateral elements through numerical examples. The DLFE and SUFE produce improved accuracy and computational efficiency. Deep learning has the meaningful potential for finite element technology. In this study, the proposed methods were only applied to develop 2D quadrilateral solid finite elements. We will enlarge the methods to various finite elements such as triangle 2D solid, plate, and shell finite elements. download the full-text of this paper (PDF, 6 pages, 560 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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