5 papers · 1 filter
Modeling Fatigue-Induced Anisotropic Quasi-Brittle Damage Based on the Endurance Surface Concept
Klas Feike, Patrick Kurzeja, Kai Langenfeld +1
This work proposes a novel continuum damage framework for fatigue based on the endurance-surface concept and uses the energy-release rate as the driving force. Damage evolution is…
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…
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…
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…
Incorporating sufficient physical information into artificial neural networks: a guaranteed improvement via physics-based Rao-Blackwellization
Gian-Luca Geuken, Jörn Mosler, Patrick Kurzeja
The concept of Rao-Blackwellization is employed to improve predictions of artificial neural networks by physical information. The error norm and the proof of improvement are transf…