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20162026
most citedNeural networks meet hyperelasticity: A guide to enforcing physics

172 citations · 620 across the 24 of their papers we have counts for

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5 papers · 1 filter

math.NT2026

Universal norms of partially de Rham -pairs and perfectness in Lubin--Tate Iwasawa theory

Marvin Schneider, Rustam Steingart

We study the behaviour of partially de Rham Selmer groups of -pairs along abelian -adic Lie extensions. In the cyclotomic case we compare them with the universal norms studie…

cond-mat.mtrl-sci2026

Reliable training of neural hyperelastic models via full-field data

Konrad Friedrichs, Franz Dammaß, Karl A. Kalina +1

We present a systematic investigation of the robustness and limitations of equilibrium gap-based calibrations for hyperelastic physics-augmented neural networks (PANNs), where we c…

cs.CE2026

Calibration of neural viscoelastic models via full-field data

Brain M. Riemer, Markus Kästner, Karl A. Kalina

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 quant…

cs.CE2026

On limitations of polyconvexity

Dominik K. Klein, Rogelio Ortigosa, Heinrich T. Roth +4

Polyconvex constitutive modeling is attractive as it guarantees stability of numerical simulations and can improve the generalization behavior of material models. However, in certa…

cs.CE2026

Advances in polyconvex anisotropic hyperelasticity

Dominik K. Klein, Karl A. Kalina, Rogelio Ortigosa +3

A key challenge in material theory is the formulation of models that satisfy all common mechanical constitutive conditions while retaining sufficient flexibility. In this context,…