3 papers
cs.CE2026
Modeling isotropic polyconvex hyperelasticity by neural networks -- sufficient and necessary criteria for compressible and incompressible materials
Gian-Luca Geuken, Patrick Kurzeja, David Wiedemann +3
This work investigates different sufficient and necessary criteria for hyperelastic, isotropic polyconvex material models, focusing on neural network implementations for compressib…
physics.comp-ph2024
Recovering Mullins damage hyperelastic behaviour with physics augmented neural networks
Martin ZlatiÄ, Marko ÄanaÄija
The aim of this work is to develop a neural network for modelling incompressible hyperelastic behaviour with isotropic damage, the so-called Mullins effect. This is obtained throug…
cs.CE2024
Data-driven methods for computational mechanics: A fair comparison between neural networks based and model-free approaches
Martin ZlatiÄ, Felipe Rocha, Laurent Stainier +1
We present a comparison between two approaches to modelling hyperelastic material behaviour using data. The first approach is a novel approach based on Data-driven Computational Me…