83 citations · 191 across the 21 of their papers we have counts for
9 papers · 1 filter
Hyperreduction accelerates finite element model updating for the discovery of neural materials
Hagen Holthusen, Matthias Knipper, Kevin Linka
Finite element model updating is among the most general approaches to discover constitutive models from structural experiments: since the displacement field is computed rather than…
Component-wise hyperreduction for nonlinear solid mechanics problems
Stephan Ritzert, Jannick Kehls, Erik Faust +3
Hyperreduced nonlinear solid-mechanics components can be generated offline and reused as transferable building blocks across different assemblies, boundary conditions, meshes, mate…
Benchmarking data-driven material models on the classic Treloar dataset
Hagen Holthusen, Moritz Flaschel, Denisa Martonová +1
Machine learning is rapidly reshaping constitutive modeling, offers new ways to learn material behavior directly from experimental data, and challenges long-established modeling pa…
Reduced integration with scaled boundary parametrization for virtual elements at finite strains
Njomza Pacolli, Bjorn Sauren, Jannick Kehls +3
This contribution presents an alternative stabilization technique for the virtual element method (VEM) based on reduced integration combined with a scaled boundary parametrization.…
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…
Adaptive Material Fingerprinting for the fast discovery of polyconvex feature combinations in isotropic and anisotropic hyperelasticity
Moritz Flaschel, Hagen Holthusen, Denisa Martonová +1
We recently proposed a method called Material Fingerprinting for the rapid discovery of mechanical material models that avoids solving continuous optimization problems. Material Fi…
Influence of the geometry on the mechanical performance of tubular interlockings: A study of the Sine Block
Domen Macek, Meike Weiß, Reymond Akpanya +3
Topological interlocking assemblies (TIA) are arrangements of blocks such that rigid-body motions of the blocks are fully constrained by their neighbours and a fixed frame. In this…
A Convex Route to Thermoelasticity: Learning Internal Energy and Dissipation
Hagen Holthusen, Paul Steinmann, Ellen Kuhl
We present a physics-based neural network framework for the discovery of constitutive models in fully coupled thermomechanics. In contrast to classical formulations based on the He…
Inelastic Constitutive Kolmogorov-Arnold Networks: A generalized framework for automated discovery of interpretable inelastic material models
Chenyi Ji, Kian P. Abdolazizi, Hagen Holthusen +2
A key problem of solid mechanics is the identification of the constitutive law of a material, that is, the relation between strain history and stress. Machine learning has lead to…