most citedPrecise, efficient and flexible modeling of crystallizing elastomers based on physics-augmented neural networks

4 citations · 4 across the 3 of their papers we have counts for

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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…

cond-mat.mtrl-sci2025★ 4 cited

Precise, efficient and flexible modeling of crystallizing elastomers based on physics-augmented neural networks

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

We propose a precise and efficient physics-augmented neural network (PANN) to model strain-induced crystallization in rubbery polymers. We demonstrate that the model can be flexibl…