most citedAn enhanced single Gaussian point continuum finite element formulation using automatic differentiation

4 citations · 4 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CE2025

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…

cs.CE2025

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…

cs.CE2025

A Comprehensive Framework for Predictive Computational Modeling of Growth and Remodeling in Tissue-Engineered Cardiovascular Implants

Mahmoud Sesa, Hagen Holthusen, Christian Böhm +3

Developing clinically viable tissue-engineered cardiovascular implants remains a formidable challenge. Achieving reliable and durable outcomes requires a deeper understanding of th…

cs.LG2025

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…

cs.CE20244 cited

An enhanced single Gaussian point continuum finite element formulation using automatic differentiation

Njomza Pacolli, Ahmad Awad, Jannick Kehls +4

This contribution presents an improved low-order 3D finite element formulation with hourglass stabilization using automatic differentiation (AD). Here, the former Q1STc formulation…