Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering
type: Item
instance of: scholarly article
title: Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering
zbMATH Open document ID: 1506.68110
author: Sourav Saha
author: Zhengtao Gan
author: Jiaying Gao
author: Orion L. Kafka
author: Xiaoyu Xie
author: Hengyang Li
author: Mahsa Tajdari
author: H. Alicia Kim
author: Ling Cheng
author: Wing K. Liu
published in: Computer Methods in Applied Mechanics and Engineering
publication date: 2021-04-26T00:00:00Z
Mathematics Subject Classification ID: 68T07
zbMATH DE Number: 7337747
zbMATH Keywords: deep learning
zbMATH Keywords: machine learning
zbMATH Keywords: reduced order model
zbMATH Keywords: data-driven discovery
zbMATH Keywords: multiscale simulation
zbMATH Keywords: artificial intelligence
zbMATH Keywords: finite element method
zbMATH Keywords: multiscale analysis
describes a project that uses: PyTorch
describes a project that uses: F3DAM
describes a project that uses: ABAQUS/Standard
describes a project that uses: Adam
describes a project that uses: MAP123-EP
MaRDI profile type: Publication
full work available at URL: j.cma.2020.113452
OpenAlex ID: W3093605970
Wikidata QID: Q115734529
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DOI: 10.1016/J.CMA.2020.113452
description: scientific article; zbMATH DE number 7337747