Deep learned finite elements

Jung Jaeho; Yoon Kyungho; Lee Phill-Seung

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Deep learned finite elements

机译：深度学习的有限元素

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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 4and 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. (C) 2020 Elsevier B.V. All rights reserved.

机译：在本文中，我们提出了一种采用深度学习的方法，一种人工智能技术，产生有限元的刚度矩阵。所提出的方法用于开发4和8节点2D固体有限元。深度学习的有限元实际上通过补丁测试和零能量模式测试。通过各种数值示例，研究了开发元件的性能并与现有元素的性能进行了比较。还研究了计算效率。证实，深度学习的有限元可以潜在地优于现有的有限元。可以应用所提出的方法来在将来生成各种类型的有限元。（c）2020 Elsevier B.v.保留所有权利。

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来源
《Computer Methods in Applied Mechanics and Engineering》 |2020年第2期|113401.1-113401.26|共26页
作者
Jung Jaeho; Yoon Kyungho; Lee Phill-Seung;
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作者单位

Korea Atom Energy Res Inst 989 Daedeok Daero Daejeon 34057 South Korea|Korea Adv Inst Sci & Technol Dept Mech Engn 291 Daehak Ro Daejeon 34141 South Korea;

Korea Adv Inst Sci & Technol Dept Mech Engn 291 Daehak Ro Daejeon 34141 South Korea;

Korea Adv Inst Sci & Technol Dept Mech Engn 291 Daehak Ro Daejeon 34141 South Korea;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Finite element; Solid element; Stiffness matrix; Artificial intelligence; Deep learning; Neural network;

机译：有限元;固体元素;刚度矩阵;人工智能;深度学习;神经网络;

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Deep learned finite elements

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