most citedAutomatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency

7 citations · 12 across the 3 of their papers we have counts for

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physics.comp-ph2025

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization

Shunyu Yin, Bernardo P. Ferreira, Gawel Kus +1

Artificial neural networks accurately learn nonlinear, path-dependent material behavior. However, training them typically requires large, diverse datasets, often created via synthe…