.png){kind=icon}
返回
Learned Gaussian quadrature for enriched solid finite elements
.png){kind=icon}
OA
AI
DOI：10.1016/j.cma.2023.116188.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We propose a novel Gaussian quadrature, referred to as the learned Gaussian quadrature, that is obtained by employing a supervised learning algorithm to find improved weights for the matrix integrations of 2D and 3D enriched solid finite elements. As the algorithm employs the intuitive relationship between a target matrix and improved Gaussian weights, it successfully finds the learned Gaussian quadrature only using a simple network. The learned Gaussian quadrature accurately calculates the matrix with fewer integration points than the standard Gaussian quadrature, thereby increasing the computational efficiency of the enriched finite elements. Using various numerical examples, the theoretical convergence behavior of the enriched solid finite elements with the learned Gaussian quadrature is first investigated, and then, the practical performances are measured. (c) 2023 Elsevier B.V. All rights reserved.
Keyword:
Enriched finite elements
Modified Gaussian quadrature
Supervised learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.3
论文数:
1.3W
被引数:
5.6W
机构
引用论文
.png){kind=icon}
Efficient and robust quadratures for isogeometric analysis: Reduced Gauss and Gauss-Greville rules用于等几何分析的高效且鲁棒的正交: 简化的Gauss和gauss-greville规则
PREAI
分享收藏Prediction of rank deficiency in partition of unity-based methods with plane triangular or quadrilateral meshes基于平面三角形或四边形网格的基于单位的方法划分中的秩不足预测
PREAI
分享收藏
查看更多内容