Convolution hierarchical deep-learning neural networks (C-HiDeNN): finite elements, isogeometric analysis, tensor decomposition, and beyond
From MaRDI portal
Publication:6164266
DOI10.1007/s00466-023-02336-5zbMath1527.74082MaRDI QIDQ6164266
Stefan Knapik, Hengyang Li, Satyajit Mojumder, Chanwook Park, Gregory J. Wagner, Lei Zhang, Daniel W. Apley, Zhongsheng Sang, Ye Lu, Wing Kam Liu, Shao-Qiang Tang
Publication date: 27 July 2023
Published in: Computational Mechanics (Search for Journal in Brave)
heat transfertopology optimizationreduced order modelingadditive manufacturingconvolution finite element methodconvolution tensor decomposition methodhigh-order smoothness
Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Finite element methods applied to problems in solid mechanics (74S05) Thermal effects in solid mechanics (74F05) Topological methods for optimization problems in solid mechanics (74P15) Numerical and other methods in solid mechanics (74S99) Isogeometric methods applied to problems in solid mechanics (74S22)
Related Items
Convolution hierarchical deep-learning neural network tensor decomposition (C-HiDeNN-TD) for high-resolution topology optimization ⋮ Convolution hierarchical deep-learning neural network (C-HiDeNN) with graphics processing unit (GPU) acceleration
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