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

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.