Stochastic approaches to structural health monitoring (SHM) are often inevitably limited by
computational constraints. For instance, for Markov chain Monte Carlo algorithms relying …

Structural health monitoring (SHM) allows the acquisition of information on the structural
integrity of any mechanical system by processing data, measured through a set of sensors …

This paper addresses the computational challenges inherent in the stochastic
characterization and uncertainty quantification of Micro-Electro-Mechanical Systems …

A major challenge in structural health monitoring (SHM) is the efficient handling of big data,
namely of high-dimensional datasets, when damage detection under environmental …

An increasing impact of micromechanically governed uncertainties is nowadays foreseen
due to the trend of progressively reducing the footprint of MEMS (microelectromechanical …

The identification method based on the traditional Proper Orthogonal Decomposition (POD)
reduced-order model has the problem of low efficiency, due to the large amount of both data …

This work is focused on the detection of interlayer damages in multi-layer composite
materials, which use the inverse analysis approach based on the reduced-order models …

Monte Carlo analyses on statistical volume elements allow quantifying the effect of
polycrystalline morphology, in terms of grain topology and orientation, on the scattering of …

Background: Due to their size, microelectromechanical systems (MEMS) display
performance indices affected by uncertainties linked to the mechanical properties and to the …

The impacts of process variations on the π-shaped thermal expansion coefficient (TEC) in-
situ test structure are analyzed. Correlations among high-dimensional design parameters …