17 citations · 23 across the 6 of their papers we have counts for
8 papers
Polyconvexity does not imply true-stress-true-strain monotonicity in the incompressible three-dimensional case
Dominik K. Klein, Maximilian P. Wollner, Patrizio Neff
We study constitutive conditions of hyperelastic potentials for incompressible material behavior in three dimensions. By means of a counterexample, we show that polyconvexity does…
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,…
Concurrent enforcement of polyconvexity and true-stress-true-strain monotonicity in incompressible isotropic hyperelasticity: application to neural network constitutive models
Maximilian P. Wollner, Dominik K. Klein, Herbert Baaser +2
The design of physics-augmented neural networks (PANNs) for the purposes of constitutive modeling has received considerable attention as of late for a variety of material behaviors…
Stable Port-Hamiltonian Neural Networks
Fabian J. Roth, Dominik K. Klein, Maximilian Kannapinn +2
In recent years, nonlinear dynamic system identification using artificial neural networks has garnered attention due to its broad potential applications across science and engineer…
Neural networks meet hyperelasticity: A monotonic approach
Dominik K. Klein, Mokarram Hossain, Konstantin Kikinov +3
We apply physics-augmented neural network (PANN) constitutive models to experimental uniaxial tensile data of rubber-like materials whose behavior depends on manufacturing paramete…
Physics-augmented neural networks for constitutive modeling of hyperelastic geometrically exact beams
Jasper O. Schommartz, Dominik K. Klein, Juan C. Alzate Cobo +1
We present neural network-based constitutive models for hyperelastic geometrically exact beams. The proposed models are physics-augmented, i.e., formulated to fulfill important mec…
Nonlinear electro-elastic finite element analysis with neural network constitutive models
Dominik K. Klein, Rogelio Ortigosa, Jesús Martínez-Frutos +1
In the present work, the applicability of physics-augmented neural network (PANN) constitutive models for complex electro-elastic finite element analysis is demonstrated. For the i…