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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

<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>

ShareScore

32/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
8
Harmonization
4
Access
16
Reuse readiness
0
Engagement
4

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