Author

M. Kästner

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Preprint Sep 2026

Reliable training of neural hyperelastic models via full-field data

It is shown that the coverage of the admissible deformation states during calibration governs the ability of a model to generalize to unseen geometries and load cases; this ability can be improved further by appropriate combinations of specimens.

K. Friedrichs, Franz Dammaß, K. Kalina et al. · 0 citations

Preprint Sep 2026

Calibration of neural viscoelastic models via full-field data

We propose an unsupervised learning framework for calibrating a physics-augmented neural network (PANN) for small-strain viscoelasticity via full-field data. It only requires quantities that are directly accessible in real experiments for training, namely global reaction forces and surface displacements. The underlying...

Brain M. Riemer, M. Kästner, K. Kalina · 1 citation