Polyconvex inelastic Constitutive Artificial Neural Networks: Source code and data
This dataset contains the source code of the polyconvex extension of the inelastic Constitutive Artificial Neural Network (iCANN) as well as the data for the examples from the publication: Holthusen, H., Lamm, L., Brepols, T., Reese, S., & E. Kuhl. Polyconvex inelastic Constitutive Artificial Ne...
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creator | Holthusen, Hagen Lamm, Lukas Brepols, Tim Reese, Stefanie Kuhl, Ellen |
description | This dataset contains the source code of the polyconvex extension of the inelastic Constitutive Artificial Neural Network (iCANN) as well as the data for the examples from the publication:
Holthusen, H., Lamm, L., Brepols, T., Reese, S., & E. Kuhl. Polyconvex inelastic Constitutive Artificial Neural Networks.
Results: Discovering a model for the polymer VHB 4910 subjected to cyclic loading
Here, we investigate the ability of the polyconvex iCANN to discover and learn a model for the material response of VHB 4910 polymer subjected to cyclic loading at different stretch rates.
The experimental data are taken from the literature:
Hossain, M., Vu, D. K., & Steinmann, P. (2012). Experimental study and numerical modelling of VHB 4910 polymer. Computational Materials Science, 59, 65-74.
https://doi.org/10.1016/j.commatsci.2012.02.027 |
doi_str_mv | 10.5281/zenodo.11084353 |
format | Dataset |
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Holthusen, H., Lamm, L., Brepols, T., Reese, S., & E. Kuhl. Polyconvex inelastic Constitutive Artificial Neural Networks.
Results: Discovering a model for the polymer VHB 4910 subjected to cyclic loading
Here, we investigate the ability of the polyconvex iCANN to discover and learn a model for the material response of VHB 4910 polymer subjected to cyclic loading at different stretch rates.
The experimental data are taken from the literature:
Hossain, M., Vu, D. K., & Steinmann, P. (2012). Experimental study and numerical modelling of VHB 4910 polymer. Computational Materials Science, 59, 65-74.
https://doi.org/10.1016/j.commatsci.2012.02.027</description><identifier>DOI: 10.5281/zenodo.11084353</identifier><language>eng</language><publisher>Zenodo</publisher><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0002-6283-935X ; 0000-0003-4760-8358 ; 0000-0002-0700-8027 ; 0000-0002-1039-1776 ; 0000-0001-8830-6132</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>776,1888</link.rule.ids><linktorsrc>$$Uhttps://commons.datacite.org/doi.org/10.5281/zenodo.11084353$$EView_record_in_DataCite.org$$FView_record_in_$$GDataCite.org$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Holthusen, Hagen</creatorcontrib><creatorcontrib>Lamm, Lukas</creatorcontrib><creatorcontrib>Brepols, Tim</creatorcontrib><creatorcontrib>Reese, Stefanie</creatorcontrib><creatorcontrib>Kuhl, Ellen</creatorcontrib><title>Polyconvex inelastic Constitutive Artificial Neural Networks: Source code and data</title><description>This dataset contains the source code of the polyconvex extension of the inelastic Constitutive Artificial Neural Network (iCANN) as well as the data for the examples from the publication:
Holthusen, H., Lamm, L., Brepols, T., Reese, S., & E. Kuhl. Polyconvex inelastic Constitutive Artificial Neural Networks.
Results: Discovering a model for the polymer VHB 4910 subjected to cyclic loading
Here, we investigate the ability of the polyconvex iCANN to discover and learn a model for the material response of VHB 4910 polymer subjected to cyclic loading at different stretch rates.
The experimental data are taken from the literature:
Hossain, M., Vu, D. K., & Steinmann, P. (2012). Experimental study and numerical modelling of VHB 4910 polymer. Computational Materials Science, 59, 65-74.
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Holthusen, H., Lamm, L., Brepols, T., Reese, S., & E. Kuhl. Polyconvex inelastic Constitutive Artificial Neural Networks.
Results: Discovering a model for the polymer VHB 4910 subjected to cyclic loading
Here, we investigate the ability of the polyconvex iCANN to discover and learn a model for the material response of VHB 4910 polymer subjected to cyclic loading at different stretch rates.
The experimental data are taken from the literature:
Hossain, M., Vu, D. K., & Steinmann, P. (2012). Experimental study and numerical modelling of VHB 4910 polymer. Computational Materials Science, 59, 65-74.
https://doi.org/10.1016/j.commatsci.2012.02.027</abstract><pub>Zenodo</pub><doi>10.5281/zenodo.11084353</doi><orcidid>https://orcid.org/0000-0002-6283-935X</orcidid><orcidid>https://orcid.org/0000-0003-4760-8358</orcidid><orcidid>https://orcid.org/0000-0002-0700-8027</orcidid><orcidid>https://orcid.org/0000-0002-1039-1776</orcidid><orcidid>https://orcid.org/0000-0001-8830-6132</orcidid><oa>free_for_read</oa></addata></record> |
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identifier | DOI: 10.5281/zenodo.11084353 |
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title | Polyconvex inelastic Constitutive Artificial Neural Networks: Source code and data |
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