TU Dresden Data Publications

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Data publications from research of Dresden University of Technology.

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Data: Neural networks meet anisotropic hyperelasticity: A framework based on generalized structure tensors and isotropic tensor functions
(Technische Universität Dresden, 2024-11-21) Kalina, Karl Alexander; Brummund, Jörg; Sun, WaiChing; Kästner, Markus
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.