Author

T. Brepols

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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

#machine learning Preprint Sep 2026

Learning the Constitutive Behavior of Materials via Neural Operators and Causal Attention: Case Studies in Plasticity and Damage

A data-driven constitutive modeling framework based on the concept of a material operator is proposed, which treats a deforming material as a functional mapping from its entire strain history to the corresponding stress response, and demonstrates accurate and robust predictions of irreversible deformation mechanisms wh...

Rishabh Arora, L. Scheunemann, T. Brepols et al. · 0 citations