8 papers
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,…
Precise, efficient and flexible modeling of crystallizing elastomers based on physics-augmented neural networks
Konrad Friedrichs, Franz DammaÃ, Karl A. Kalina +1
We propose a precise and efficient physics-augmented neural network (PANN) to model strain-induced crystallization in rubbery polymers. We demonstrate that the model can be flexibl…
Construction of minimal integrity basis for anisotropic hyperelasticity via structural tensors
Brain M. Riemer, Jörg Brummund, Karl A. Kalina +3
We present minimal integrity bases for all common anisotropies in hyperelasticity via the structural tensor concept, which can be used to formulate any algebraic invariant function…
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…