Convolution hierarchical deep-learning neural network tensor decomposition (C-HiDeNN-TD) for high-resolution topology optimization

DOI10.1007/s00466-023-02333-8zbMath1524.74386WikidataQ122236535 ScholiaQ122236535MaRDI QIDQ6164267

Ye Lu, Daniel W. Apley, Stefan Knapik, Satyajit Mojumder, Chanwook Park, Hengyang Li, Wing Kam Liu, Wei Chen, Yangfan Li, Jiachen Guo

Publication date: 27 July 2023

Published in: Computational Mechanics (Search for Journal in Brave)

zbMATH Keywords

reduced-order modeling3D printing and metamaterial designhierarchical deep-learning neural networks with GPUhigh resolution topology optimizationmulti-scale concurrent design

Mathematics Subject Classification ID

Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Topological methods for optimization problems in solid mechanics (74P15)

Related Items (2)

Convolution hierarchical deep-learning neural networks (C-HiDeNN): finite elements, isogeometric analysis, tensor decomposition, and beyond ⋮ Convolution hierarchical deep-learning neural network (C-HiDeNN) with graphics processing unit (GPU) acceleration

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