Data-Driven Uncertainty Quantification and Propagation in Structural Dynamics Inverse Problems, Ph.D. Dissertation Sharif University of Technology ; Rahimzadeh Rofooei, Fayaz (Supervisor) ; Katafygiotis, Lambros (Supervisor)
Abstract
This study opens up new horizons in data-driven structural identification methods offering extensive improvements over the existing time-/frequency-domain probabilistic methods. It pushes forward a holistic Bayesian statistical framework to integrate the existing formulations under a hierarchical setting aiming to quantify both the identification precision and the ensemble variability prompted due to model errors. Since the computation of the posterior distributions in hierarchical models is expensive and cumbersome, novel marginalization strategies, asymptotic approximations, and maximum a posteriori estimations are proposed offering mathematical formulations for the uncertainty...
Cataloging briefData-Driven Uncertainty Quantification and Propagation in Structural Dynamics Inverse Problems, Ph.D. Dissertation Sharif University of Technology ; Rahimzadeh Rofooei, Fayaz (Supervisor) ; Katafygiotis, Lambros (Supervisor)
Abstract
This study opens up new horizons in data-driven structural identification methods offering extensive improvements over the existing time-/frequency-domain probabilistic methods. It pushes forward a holistic Bayesian statistical framework to integrate the existing formulations under a hierarchical setting aiming to quantify both the identification precision and the ensemble variability prompted due to model errors. Since the computation of the posterior distributions in hierarchical models is expensive and cumbersome, novel marginalization strategies, asymptotic approximations, and maximum a posteriori estimations are proposed offering mathematical formulations for the uncertainty...
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