research data . Dataset . 2020

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;
Open Access
  • Published: 21 Jun 2020
  • Publisher: Zenodo
Abstract
<pre>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 = &quot;A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths&quot;, journal = &quot;Computer Methods in Applied Mechanics and Engineering&quot;, pages = &quot; 113234&quot;, year = &quot;2020&quot;, issn = &quot;0045-7825&quot;, doi = &quot;https://doi.org/10.1016/j.cma.2020.113234&quot;, author = &quot;Wu, Ling and Nguyen, Van Dung and Kilingar, Nanda Gopala and Noels, Ludovic&quot;</pre>
Subjects
free text keywords: Artificial Neural Network, Recurrent Neural Network, Surrogate, Multi-scale, Elasto-plasticity, Data-driven, Science Policy, Space Science, Biological Sciences not elsewhere classified
Funded by
EC| MOAMMM
Project
MOAMMM
Multi-scale Optimisation for Additive Manufacturing of fatigue resistant shock-absorbing MetaMaterials
  • Funder: European Commission (EC)
  • Project Code: 862015
  • Funding stream: H2020 | RIA
Validated by funder
Communities
FET H2020FET OPEN: FET-Open Challenging Current Thinking
FET H2020FET OPEN: Multi-scale Optimisation for Additive Manufacturing of fatigue resistant shock-absorbing MetaMaterials
Science and Innovation Policy Studies
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Dataset . 2020
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Dataset . 2020
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Dataset . 2020
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