1 citations · 1 across the 7 of their papers we have counts for
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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…
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 WeiÃ, 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 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…