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.
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