A multi-fidelity Bayesian recurrent neural network framework predicts history-dependent material responses while separately quantifying aleatoric and epistemic uncertainties.
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Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling
A multi-fidelity Bayesian recurrent neural network framework predicts history-dependent material responses while separately quantifying aleatoric and epistemic uncertainties.