In view of the universal existence of multi-source uncertainty factors in engineering
structures, a novel method of dynamic force reconstruction is investigated based on Artificial …

The establishment of a Digital Twin of an operating engineered system can increase the
potency of Structural Health Monitoring (SHM) tools, which are then bestowed with …

The input-parameter-state estimation capabilities of a novel unscented Kalman filter is
examined herein on both linear and nonlinear systems. The unknown input is estimated in …

The use of machine learning in structural health monitoring is becoming more common, as
many of the inherent tasks (such as regression and classification) in develo** condition …

Abstract Floating Modular Energy Islands (FMEIs) are modularized, interconnected floating
structures that function together to produce, store, possibly convert and transport renewable …

In this article, an overview of Bayesian methods for sequential simulation from posterior
distributions of nonlinear and non-Gaussian dynamic systems is presented. The focus is …

Measured data from a dynamical system can be assimilated into a predictive model by
means of Kalman filters. Nonlinear extensions of the Kalman filter, such as the Extended …

The intersection of physics and machine learning has given rise to the physics-enhanced
machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the …

An optimal sensor placement (OSP) framework for virtual sensing using the augmented
Kalman Filter (AKF) technique is presented based on information and utility theory. The …

This contribution presents a hierarchical Bayesian filter for recursive input, state and
parameter estimation using spatially incomplete and noisy output-only vibration …