5 papers
Watching Physics: the Generative Science of Matter and Motion
Hagen Holthusen, Kevin Linka, Ellen Kuhl
Can we learn the physics of matter in motion directly from images and video--and trust it? Answering this question requires integrating experiments, physics-based simulation, and d…
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…
Atrial constitutive neural networks
Mathias Peirlinck, Kevin Linka, Ellen Kuhl
This work presents a novel approach for characterizing the mechanical behavior of atrial tissue using constitutive neural networks. Based on experimental biaxial tensile test data…
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…
Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling
Hagen Holthusen, Tim Brepols, Kevin Linka +1
Soft biological tissues exhibit a tendency to maintain a preferred state of tensile stress, known as tensional homeostasis, which is restored even after external mechanical stimuli…