Dataset Open Access

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, Ling; Nguyen, Van Dung; Kilingar, Nanda Gopala; Noels, Ludovic


Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Wu, Ling</dc:creator>
  <dc:creator>Nguyen, Van Dung</dc:creator>
  <dc:creator>Kilingar, Nanda Gopala</dc:creator>
  <dc:creator>Noels, Ludovic</dc:creator>
  <dc:date>2020-06-21</dc:date>
  <dc:description>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data) 
title = "A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths",
journal = "Computer Methods in Applied Mechanics and Engineering",
pages = " 113234",
year = "2020",
issn = "0045-7825",
doi = "https://doi.org/10.1016/j.cma.2020.113234",
author = "Wu, Ling and Nguyen, Van Dung and Kilingar, Nanda Gopala and Noels, Ludovic"</dc:description>
  <dc:description>This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 862015 for the project "Multi-scale Optimisation for Additive Manufacturing of fatigue resistant shock-absorbing MetaMaterials (MOAMMM)" of the H2020-EU.1.2.1. -FET Open Programme.

N.G. Kilingar was financed by the EnlightenIt project, grant number PDR T.0038.16 of FRS-FNRS.

V.D. Nguyen was a Postdoctoral Researcher at the Belgian National Fund for Scientific Research (FNRS)</dc:description>
  <dc:identifier>https://zenodo.org/record/3902663</dc:identifier>
  <dc:identifier>10.5281/zenodo.3902663</dc:identifier>
  <dc:identifier>oai:zenodo.org:3902663</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/Horizon 2020 Framework Programme - Research and Innovation action/862015/</dc:relation>
  <dc:relation>handle:10.1016/j.cma.2020.113234</dc:relation>
  <dc:relation>doi:10.5281/zenodo.3902662</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:source>Computer Methods in Applied Mechanics and Engineering 113234</dc:source>
  <dc:subject>Artificial Neural Network</dc:subject>
  <dc:subject>Recurrent Neural Network</dc:subject>
  <dc:subject>Surrogate</dc:subject>
  <dc:subject>Multi-scale</dc:subject>
  <dc:subject>Elasto-plasticity</dc:subject>
  <dc:subject>Data-driven</dc:subject>
  <dc:title>Data of A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>
280
88
views
downloads
All versions This version
Views 280280
Downloads 8888
Data volume 26.2 GB26.2 GB
Unique views 247247
Unique downloads 6767

Share

Cite as