7 papers · 1 filter
On limitations of polyconvexity
Dominik K. Klein, Rogelio Ortigosa, Heinrich T. Roth +4
Polyconvex constitutive modeling is attractive as it guarantees stability of numerical simulations and can improve the generalization behavior of material models. However, in certa…
Advances in polyconvex anisotropic hyperelasticity
Dominik K. Klein, Karl A. Kalina, Rogelio Ortigosa +3
A key challenge in material theory is the formulation of models that satisfy all common mechanical constitutive conditions while retaining sufficient flexibility. In this context,…
A physics-augmented neural network framework for finite strain incompressible viscoelasticity
Karl A. Kalina, Jörg Brummund, Markus Kästner
We propose a physics-augmented neural network (PANN) framework for finite strain incompressible viscoelasticity within the generalized standard materials theory. The formulation is…
A data-driven multiscale scheme for anisotropic finite strain magneto-elasticity
Heinrich T. Roth, Philipp Gebhart, Karl A. Kalina +2
In this work, we develop a neural network-based, data-driven, decoupled multiscale scheme for the modeling of structured magnetically soft magnetorheological elastomers (MREs). On…
Data-efficient inverse design of spinodoid metamaterials
Max Rosenkranz, Markus Kästner, Ivo F. Sbalzarini
We create an data-efficient and accurate surrogate model for structure-property linkages of spinodoid metamaterials with only 75 data points -- far fewer than the several thousands…
A dual-stage constitutive modeling framework based on finite strain data-driven identification and physics-augmented neural networks
Lennart Linden, Karl A. Kalina, Jörg Brummund +2
In this contribution, we present a novel consistent dual-stage approach for the automated generation of hyperelastic constitutive models which only requires experimentally measurab…