Author

H. Holthusen

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Open access Oct 2026

Autoencoder-based non-intrusive model order reduction for anisotropic and multi-physics problems

We propose a non-intrusive, Autoencoder-based framework for reduced-order modeling in continuum mechanics. Our method integrates three stages: (i) an unsupervised Autoencoder compresses high-dimensional finite element solutions into a compact latent space, (ii) a supervised regression network maps problem parameters...

Jannick Kehls, Ellen Kuhl, T. Brepols et al. · Computational Mechanics · 0 citations

Preprint Aug 2026

Component-wise hyperreduction for nonlinear solid mechanics problems

Hyperreduced nonlinear solid-mechanics components can be generated offline and reused as transferable building blocks across different assemblies, boundary conditions, meshes, material parameters, and constitutive models. We use proper orthogonal decomposition (POD) and energy conserving sampling and weighting (ECSW) o...

S. Ritzert, Jannick Kehls, Erik Faust et al. · 0 citations

Preprint Aug 2026

Benchmarking data-driven material models on the classic Treloar dataset

The classic experimental data of Treloar is used to benchmark popular frameworks for hyperelasticity: (Generalized-Invariant) Constitutive Artificial Neural Networks, Physics-Augmented Neural Networks, (Adaptive) Material Fingerprinting, and Efficient Unsupervised Constitutive Law Identification&Discovery and highlight...

H. Holthusen, Moritz Flaschel, Denisa Martonová et al. · 0 citations