7 citations · 12 across the 3 of their papers we have counts for
3 papers
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…
cs.LG2025★ 5 cited
Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling
Jiaxiang Yi, Bernardo P. Ferreira, Miguel A. Bessa
Data-driven learning is generalized to consider history-dependent multi-fidelity data, while quantifying epistemic uncertainty and disentangling it from data noise (aleatoric uncer…
math.NA2025★ 7 cited
Automatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency
Bernardo P. Ferreira, Miguel A. Bessa
We introduce the first Automatically Differentiable Model Updating (ADiMU) framework that finds any history-dependent material model from full-field displacement and global force d…