
The author presents a novel framework combining two methods to quantify uncertainties in engineered systems, focusing on aleatoric and epistemic sources. The approach involves machine learning to transform noisy datasets into distributions for data-consistent inversion.


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from-displacements-to-distributions-a-machine-learning-framework-for-uncertainty-quantification-in-computational-models


From Displacements to Distributions: A Machine-Learning Framework for Uncertainty Quantification in Computational Models
