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Convolution Hierarchical Deep-learning Neural Networks (C-HiDeNN): finite elements, isogeometric analysis, tensor decomposition, and beyond 期刊论文
COMPUTATIONAL MECHANICS, 2023./通讯作者：Lu, Y, Liu, WK
Authors:  Lu, Ye;  Li, Hengyang;  Zhang L(张磊);  Park, Chanwook;  Mojumder, Satyajit;  Knapik, Stefan;  Sang, Zhongsheng;  Tang, Shaoqiang;  Apley, DanielW;  Wagner, GregoryJ;  Liu, WingKam
Adobe PDF(5358Kb)  |  Favorite  |  View/Download：155/50  |  Submit date：2023/06/15
Convolution FEM and HiDeNN  Tensor decomposition  Reduced order modeling  Additive manufacturing  High-order smoothness  Isogeometric analysis (IGA)  
HiDeNN-TD: Reduced-order hierarchical deep learning neural networks 期刊论文
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2022, 卷号: 389, 页码: 33./通讯作者：Tang, Shaoqiang, Liu, Wing Kam
Authors:  Zhang L(张磊);  Lu, Ye;  Tang, Shaoqiang;  Liu, Wing Kam
Adobe PDF(4228Kb)  |  Favorite  |  View/Download：358/98  |  Submit date：2022/02/17
Hierarchical deep-learning neural networks  Proper generalized decomposition  Canonical tensor decomposition  Reduced order finite element method  Convergence study and error bound  
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