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…
cs.CE2025
Limits of isotropic damage models for complex load paths -- beyond stress triaxiality and Lode angle parameter
K. Feike, P. Kurzeja, J. Mosler +1
The stress triaxiality and the Lode angle parameter are two well established stress invariants for the characterization of damage evolution. This work assesses the limits of this t…
cs.CE2025
Input convex neural networks: universal approximation theorem and implementation for isotropic polyconvex hyperelastic energies
Gian-Luca Geuken, Patrick Kurzeja, David Wiedemann +1
This paper presents a novel framework of neural networks for isotropic hyperelasticity that enforces necessary physical and mathematical constraints while simultaneously satisfying…