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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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…