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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

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    <dct:description>&lt;pre&gt;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 = &amp;quot;A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths&amp;quot;, journal = &amp;quot;Computer Methods in Applied Mechanics and Engineering&amp;quot;, pages = &amp;quot; 113234&amp;quot;, year = &amp;quot;2020&amp;quot;, issn = &amp;quot;0045-7825&amp;quot;, doi = &amp;quot;https://doi.org/10.1016/j.cma.2020.113234&amp;quot;, author = &amp;quot;Wu, Ling and Nguyen, Van Dung and Kilingar, Nanda Gopala and Noels, Ludovic&amp;quot;&lt;/pre&gt;</dct:description>
    <dct: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)</dct:description>
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June 21, 2020
DOI:
10.5281/zenodo.3902663

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Keyword(s):
Artificial Neural Network Recurrent Neural Network Surrogate Multi-scale Elasto-plasticity Data-driven
Published in:
Computer Methods in Applied Mechanics and Engineering: pp. 113234.
Grants:
European Commission:
  • MOAMMM - Multi-scale Optimisation for Additive Manufacturing of fatigue resistant shock-absorbing MetaMaterials (862015)
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License (for files):
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