287 citations · 517 across the 10 of their papers we have counts for
10 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…
Best-in-class modeling: A novel strategy to discover constitutive models for soft matter systems
Kevin Linka, Ellen Kuhl
The ability to automatically discover interpretable mathematical models from data could forever change how we model soft matter systems. For convex discovery problems with a unique…
On sparse regression, Lp-regularization, and automated model discovery
Jeremy A. McCulloch, Skyler R. St. Pierre, Kevin Linka +1
Sparse regression and feature extraction are the cornerstones of knowledge discovery from massive data. Their goal is to discover interpretable and predictive models that provide s…
Benchmarks for physics-informed data-driven hyperelasticity
Vahidullah Tac, Kevin Linka, Francisco Sahli-Costabal +2
Data-driven methods have changed the way we understand and model materials. However, while providing unmatched flexibility, these methods have limitations such as reduced capacity…
A new family of Constitutive Artificial Neural Networks towards automated model discovery
Kevin Linka, Ellen Kuhl
For more than 100 years, chemical, physical, and material scientists have proposed competing constitutive models to best characterize the behavior of natural and man-made materials…
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems
Kevin Linka, Amelie Schafer, Xuhui Meng +3
Understanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines, machine learning has advanced as the go-to technology to analyze non…