A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths-Reference-Cited by-同舟云学术

A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths

Published:2020-09 Issue: Volume:369 Page:113234
ISSN:0045-7825
Container-title:Computer Methods in Applied Mechanics and Engineering
language:en
Short-container-title:Computer Methods in Applied Mechanics and Engineering

Author:

Wu Ling^ORCID,Nguyen Van Dung,Kilingar Nanda Gopala,Noels Ludovic

Funder

European Union’s Horizon 2020

FRS-FNRS, Belgium

Publisher

Elsevier BV

Subject

Computer Science Applications,General Physics and Astronomy,Mechanical Engineering,Mechanics of Materials,Computational Mechanics

Reference43 articles.

1. Data of A Recurrent Neural Network-Accelerated Multi-Scale Model for Elasto-Plastic Heterogeneous Materials Subjected to Random Cyclic and Non- Proportional Loading Paths;Wu,2020

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3. Effective properties;Noels,2016

4. Solid mechanics and its applications;Yvonnet,2019

5. Computational homogenization for heat conduction in heterogeneous solids;Özdemir;Internat. J. Numer. Methods Engrg.,2008

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