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Data of "Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step."

<p>Data related to<br> ===========<br> title = &quot;Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step.&quot;,<br> journal = &quot;Computer Methods in Applied Mechanics and Engineering&quot;,<br> volume =&quot;390&quot;,<br> year = &quot;2022&quot;,<br> doi = &quot;https://doi.org/<a href="http://dx.doi.org/10.1016/j.cma.2021.114476">10.1016/j.cma.2021.114476</a> &quot;,<br> pages = &quot;114476 &quot;,<br> author = &quot;Wu, Ling and Noels, Ludovic&quot;</p> <p>We would be grateful if you could cite the paper in the case in which you are using the data</p> <p>&nbsp;</p> <p>The files replace version 1 whose zip was corrupted.</p> <p>&nbsp;</p>

ShareScore

40/100

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
8
Engagement
4

Topics