Sampling methods for solving Bayesian model updating problems: A tutorial
This tutorial paper reviews the use of advanced Monte Carlo sampling methods in the
context of Bayesian model updating for engineering applications. Markov Chain Monte …
context of Bayesian model updating for engineering applications. Markov Chain Monte …
Sequential Bayesian inference for uncertain nonlinear dynamic systems: a tutorial
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 …
distributions of nonlinear and non-Gaussian dynamic systems is presented. The focus is …
Digital twin technology for wind turbine towers based on joint load–response estimation: A laboratory experimental study
An accurate estimation of dynamic loads and structural dynamic responses is deemed an
indispensable prerequisite for developing trustworthy digital twin (DT) models of dynamically …
indispensable prerequisite for developing trustworthy digital twin (DT) models of dynamically …
Physics-informed machine learning for structural health monitoring
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 developing condition …
many of the inherent tasks (such as regression and classification) in developing condition …
Digital twin approach for on-load tap changers using data-driven dynamic model updating and optimization-based operating condition estimation
On-load tap changers (OLTCs), which are found in power transformers, are mechanically
operating components. The vibration signal of an OLTC can provide effective observed data …
operating components. The vibration signal of an OLTC can provide effective observed data …
Extraction of contact-point response in indirect bridge health monitoring using an input estimation approach
R Nayek, S Narasimhan - Journal of Civil Structural Health Monitoring, 2020 - Springer
Identification of bridge dynamic properties from moving vehicle responses presents several
practical benefits. However, a problem that arises when working with vehicle responses for …
practical benefits. However, a problem that arises when working with vehicle responses for …
Gaussian process priors for systems of linear partial differential equations with constant coefficients
M Harkonen, M Lange-Hegermann… - … on machine learning, 2023 - proceedings.mlr.press
Partial differential equations (PDEs) are important tools to model physical systems and
including them into machine learning models is an important way of incorporating physical …
including them into machine learning models is an important way of incorporating physical …
Input-state-parameter-noise identification and virtual sensing in dynamical systems: A Bayesian expectation-maximization (BEM) perspective
Structural identification and damage detection can be generalized as the simultaneous
estimation of input forces, physical parameters, and dynamical states. Although Kalman-type …
estimation of input forces, physical parameters, and dynamical states. Although Kalman-type …
A spectrum of physics-informed Gaussian processes for regression in engineering
Despite the growing availability of sensing and data in general, we remain unable to fully
characterize many in-service engineering systems and structures from a purely data-driven …
characterize many in-service engineering systems and structures from a purely data-driven …
Assessment of alternative covariance functions for joint input-state estimation via Gaussian Process latent force models in structural dynamics
Digital technologies can be used to gather accurate information about the behavior of
structural components for improving systems design, as well as for enabling advanced …
structural components for improving systems design, as well as for enabling advanced …