172 citations · 292 across the 4 of their papers we have counts for
4 papers · 1 filter
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…
Neural network-based multiscale modeling of finite strain magneto-elasticity with relaxed convexity criteria
Karl A. Kalina, Philipp Gebhart, Jörg Brummund +3
We present a framework for the multiscale modeling of finite strain magneto-elasticity based on physics-augmented neural networks (NNs). By using a set of problem specific invarian…
Neural networks meet hyperelasticity: A guide to enforcing physics
Lennart Linden, Dominik K. Klein, Karl A. Kalina +3
In the present work, a hyperelastic constitutive model based on neural networks is proposed which fulfills all common constitutive conditions by construction, and in particular, is…
FE An efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining
Karl A. Kalina, Lennart Linden, Jörg Brummund +1
Herein, we present a new data-driven multiscale framework called FE which is based on two main keystones: the usage of physics-constrained artificial neural networks…