Data: Neural networks meet anisotropic hyperelasticity: A framework based on generalized structure tensors and isotropic tensor functions
References to related material | https://doi.org/10.48550/arXiv.2410.03378 | |
Type of the data | Dataset | |
Total size of the dataset | 9018307 | |
Author | Kalina, Karl Alexander | |
Author | Brummund, Jörg | |
Author | Sun, WaiChing | |
Author | Kästner, Markus | |
Upload date | 2024-11-21T06:58:03Z | |
Publication date | 2024-11-21T06:58:03Z | |
Publication date | 2024-11-21 | |
Abstract of the dataset | This collection provides homogenized datasets including deformation gradient, free energy, stress tensor and material tangent for anisotropic hyperelastic composites. Five different representative volume elements (RVEs) are included: An RVE of a fiber reinforced material with stochastic fiber distribution (stochastic fibers), a unit cell with a hexagonal fiber arrangement (hexagonal fibers), a unit cell with one spherical inclusion (cubic sphere), an RVE with a plane-like arrangement of particles (plane spheres), and an RVE with an arrangement of particles in a chain-like structure (chain spheres). All components, i.e., matrix, particles and fibers are assumed to be compressible and isotropic. For all, a two-parametric neo-Hookean model was chosen. The data have been generated by using an in-house finite element code based on Matlab. The data belongs to the work "Neural networks meet anisotropic hyperelasticity: A framework based on generalized structure tensors and isotropic tensor functions" by Kalina et al.; https://doi.org/10.48550/arXiv.2410.03378. Further information on the data can be found there. | |
Public reference to this page | https://opara.zih.tu-dresden.de/handle/123456789/1089 | |
Public reference to this page | https://doi.org/10.25532/OPARA-677 | |
Publisher | Technische Universität Dresden | |
Licence | Attribution-NoDerivatives 4.0 International | en |
URI of the licence text | http://creativecommons.org/licenses/by-nd/4.0/ | |
Specification of the discipline(s) | 4::41::402::402-02 | |
Title of the dataset | Data: Neural networks meet anisotropic hyperelasticity: A framework based on generalized structure tensors and isotropic tensor functions |
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