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Empirical Material Sampling and Linearisation -- A Simple and Efficient Strain-Space Model Order Reduction Approach for Computational Homogenisation in Large-Deformation Hyperelasticity0citations
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Explainable Artificial Intelligence for Mechanics: Physics-Explaining Neural Networks for Constitutive Models2022 Frontiers in Materials article Physics and Astronomy Model Reduction and Neural Networks Open access
Arnd Koeppe, Franz Bamer, Michael A. Selzer, Britta Nestler, Bernd A. Markert
41citations -
A hyper-reduction method using adaptivity to cut the assembly costs of reduced order models2021 Computer Methods in Applied Mechanics and Engineering article Physics and Astronomy Model Reduction and Neural Networks Open access
Jack Hale, Elisa Schenone, Davide Baroli, Lars Beex, Stéphane Bordas
13citations -
Pitfalls of Projection: A study of Newton-type solvers for incremental potentials2026 ACM Transactions on Graphics article Engineering Advanced Numerical Methods in Computational Mathematics Open access
Andreas Longva, Fabian Löschner, José Antonio Fernández-Fernández, Egor Larionov, Uri M. Ascher, Jan Bender
2citations -
A two-stage surrogate model for Neo-Hookean problems based on adaptive proper orthogonal decomposition and hierarchical tensor approximation2020 Computer Methods in Applied Mechanics and Engineering article Physics and Astronomy Model Reduction and Neural Networks
Steffen Kastian, Dieter Moser, Lars Grasedyck, Stefanie Reese
18citations -
An open source pipeline for design of experiments for hyperelastic models of the skin with applications to keloids2020 Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials article Engineering Elasticity and Material Modeling
Danas Sutula, Aflah Elouneg, M. Sensale, Franz Chouly, Jérôme Chambert, Arnaud Lejeune, +4 more
17citations