Πλοήγηση ανά Θέμα "Covariance matrix"
Αποτελέσματα 1-17 από 17
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Adaptive Bayesian Inference Framework for Joint Model and Noise Identification
(2022)Model updating, the process of inferring a model from data, is prone to the adverse effects of modeling error, which is caused by simplification and idealization assumptions in the mathematical models. In this study, an ... -
Adaptive Kalman filters for nonlinear finite element model updating
(2020)This paper presents two adaptive Kalman filters (KFs) for nonlinear model updating where, in addition to nonlinear model parameters, the covariance matrix of measurement noise is estimated recursively in a near online ... -
An analytical perspective on Bayesian uncertainty quantification and propagation in mode shape assembly
(2020)Assembling local mode shapes identified from multiple setups to form global mode shapes is of practical importance when the degrees of freedom (dofs) of interest are measured separately in individual setups or when one ... -
A Bayesian Expectation-Maximization (BEM) methodology for joint input-state estimation and virtual sensing of structures
(2022)The joint input-state estimation and virtual sensing of structures are reformulated on a Bayesian probabilistic foundation, focusing on data-driven uncertainty quantification and propagation. The variation of input forces ... -
Bayesian uncertainty quantification and propagation using adjoint techniques
(2014)This paper presents the Bayesian inference framework enhanced by analytical approximations for uncertainty quantification and propagation and parameter estimation. A Gaussian distribution is used to approximate the posterior ... -
Bayesian uncertainty quantification of turbulence models based on high-order adjoint
(2015)The uncertainties in the parameters of turbulence models employed in computational fluid dynamics simulations are quantified using the Bayesian inference framework and analytical approximations. The posterior distribution ... -
Blood flow and diameter effect in the navigation process of magnetic nanocarriers inside the carotid artery
(2022)Background and objective: Serious side effects are occurred during the cancer therapy. Magnetic driving of nanoparticles is a novel method for the elimination of these effects by supplying with anticancer drug or increase ... -
A computational framework of kinematic accuracy reliability analysis for industrial robots
(2020)A new computational method to evaluate comprehensively the positional accuracy reliability for single coordinate, single point, multipoint and trajectory accuracy of industrial robots is proposed using the sparse grid ... -
Data-driven uncertainty quantification and propagation in structural dynamics through a hierarchical Bayesian framework
(2020)In the presence of modeling errors, the mainstream Bayesian methods seldom give a realistic account of uncertainties as they commonly underestimate the inherent variability of parameters. This problem is not due to any ... -
Exploiting task-based parallelism in Bayesian Uncertainty Quantification
(2015)We introduce a task-parallel framework for non-intrusive Bayesian Uncertainty Quantification and Propagation of complex and computationally demanding physical models on massively parallel computing architectures. The ... -
Hierarchical Bayesian operational modal analysis: Theory and computations
(2020)This paper presents a hierarchical Bayesian modeling framework for the uncertainty quantification in modal identification of linear dynamical systems using multiple vibration data sets. This novel framework integrates the ... -
A new online Bayesian approach for the joint estimation of state and input forces using response-only measurements
(2019)In this paper, a recursive Bayesian-filtering technique is presented for the joint estimation of the state and input forces. By introducing new prior distributions for the input forces, the direct transmission of the input ... -
Optimal sensor placement for response predictions using local and global methods
(2020)A Bayesian framework for model-based optimal sensor placement for response predictions is presented. Our interest lies in determining the parameters of the model in order to make predictions about a particular response ... -
Optimal sensor placement for response reconstruction in structural dynamics
(2020)A framework for optimal sensor placement (OSP) for response reconstruction under uncertainty is presented based on information theory. The OSP is selected as the one that maximizes an expected utility function taken as the ... -
Real-time Bayesian parameter, state and input estimation using output-only vibration measurements
(2020)This paper presents a new sequential Bayesian method for the real-time estimation of state, input, parameters, and noise characteristics in dynamical systems using output-only measurements. It is an extension of the method ... -
Sequential Bayesian estimation of state and input in dynamical systems using output-only measurements
(2019)The problem of joint estimation of the state and input in linear time-invariant dynamical systems is revisited proposing novel sequential Bayesian formulations. An appealing feature of the proposed method is the promise ... -
A unified sampling-based framework for optimal sensor placement considering parameter and prediction inference
(2021)We present a Bayesian framework for model-based optimal sensor placement. Our interest lies in minimizing the uncertainty on predictions of a particular response quantity of interest, with parameter estimation being an ...