83 citations · 122 across the 11 of their papers we have counts for
4 papers · 1 filter
A Complement to Neural Networks for Anisotropic Inelasticity at Finite Strains
Hagen Holthusen, Ellen Kuhl
We propose a complement to constitutive modeling that augments neural networks with material principles to capture anisotropy and inelasticity at finite strains. The key element is…
Generalized invariants meet constitutive neural networks: A novel framework for hyperelastic materials
Denisa Martonová, Alain Goriely, Ellen Kuhl
The major challenge in determining a hyperelastic model for a given material is the choice of invariants and the selection how the strain energy function depends functionally on th…
Autoencoder-based non-intrusive model order reduction in continuum mechanics
Jannick Kehls, Ellen Kuhl, Tim Brepols +2
We propose a non-intrusive, Autoencoder-based framework for reduced-order modeling in continuum mechanics. Our method integrates three stages: (i) an unsupervised Autoencoder compr…
A generalized dual potential for inelastic Constitutive Artificial Neural Networks: A JAX implementation at finite strains
Hagen Holthusen, Kevin Linka, Ellen Kuhl +1
We present a methodology for designing a generalized dual potential, or pseudo potential, for inelastic Constitutive Artificial Neural Networks (iCANNs). This potential, expressed…