Browsing by Subject "Parameter estimation"
Now showing items 120 of 47

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 ... 
A bayesian identification methodology for selection among Pareto optimal structural models using modal residuals
(2005)The structural parameter estimation problem based on measured modal data is formulated as a multiobjective optimization problem in which modal metrics measuring the fit between measured and model predicted groups of modal ... 
Bayesian inference for damage identification based on analytical probabilistic model of scattering coefficient estimators and ultrafast wave scattering simulation scheme
(2020)Ultrasonic Guided Waves (GW) actuated by piezoelectric transducers installed on structures have proven to be sensitive to small structural defects, with acquired scattering signatures being dependent on the damage type. ... 
Bayesian optimal estimation for outputonly nonlinear system and damage identification of civil structures
(2018)This paper presents a new framework for outputonly nonlinear system and damage identification of civil structures. This framework is based on nonlinear finite element (FE) model updating in the timedomain, using only the ... 
Bayesian optimal experimental design for parameter estimation and response predictions in complex dynamical systems
(2017)A Bayesian optimal experimental design (OED) framework is revisited and applied to a number of structural dynamics problems. The objective is to optimize the design of the experiment such that the most informative data are ... 
Bayesian optimal experimental design using asymptotic approximations
(2017)Bayesian optimal experimental design (OED) tools for model parameter estimation and response predictions in structural dynamics include sampling (Huan and Marzouk, J. Comput. Phys., 232:288–317, 2013) and asymptotic ... 
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 highorder 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 ... 
Clinical validation of the LKB model and parameter sets for predicting radiationinduced pneumonitis from breast cancer radiotherapy
(2006)The choice of the appropriate model and parameter set in determining the relation between the incidence of radiation pneumonitis and dose distribution in the lung is of great importance, especially in the case of breast ... 
Computationally efficient hierarchical Bayesian modeling framework for learning embedded model uncertainties
(2020)A hierarchical Bayesian modeling (HBM) framework has recently been developed for estimating the uncertainties in the parameters of physicsbased models of systems, as well as propagating these uncertainties to estimate the ... 
Data featuresbased likelihoodinformed Bayesian finite element model updating
(2019)A new formulation for likelihoodinformed Bayesian inference is proposed in this work based on probability models introduced for the features between the measurements and model predictions. The formulation applies to both ... 
The effect of prediction error correlation on optimal sensor placement in structural dynamics
(2012)The problem of estimating the optimal sensor locations for parameter estimation in structural dynamics is revisited. The effect of spatially correlated prediction errors on the optimal sensor placement is investigated. ... 
Energy and Spectrum Efficient Parameter Estimation in Wireless Body Sensor Networks
(2015)In this letter we consider the problem of linear distributed estimation (DES) of a random parameter in a wireless body sensor network (BSN). We propose a novel architecture, and an associated optimization model, for DES ... 
Estimation of models for cumulative infiltration of soil using machine learning methods
(2021)Knowledge of cumulative infiltration of soil is necessary for irrigation, surface flow, groundwater recharge and many other hydrological processes. In the present study, the Support Vector Machine (SVM), artificial neural ... 
A fast Bayesian inference scheme for identification of local structural properties of layered composites based on wave and finite elementassisted metamodeling strategy and ultrasound measurements
(2020)Reliable verification and evaluation of the mechanical properties of a layered composite ensemble are critical for industrially relevant applications, however it still remains an open engineering challenge. In this study, ... 
Hierarchical bayesian model updating for probabilistic damage identification
(2015)This paper presents the newly developed Hierarchical Bayesian model updating method for identification of civil structures. The proposed updating method is suitable for uncertainty quantification of model updating parameters, ... 
Hierarchical Bayesian modeling framework for model updating and robust predictions in structural dynamics using modal features
(2022)The hierarchical Bayesian modeling (HBM) framework has recently been developed to tackle the uncertainty quantification and propagation in structural dynamics inverse problems. This new framework characterizes the ensemble ... 
Hierarchical Bayesian uncertainty quantification of dynamical models utilizing modal statistical information
(2020)Updating dynamical models based on experimental modal information has become an important topic in structural health monitoring. This paper revisits this significant problem and develops a new twostage hierarchical Bayesian ... 
Hierarchical Bayesian uncertainty quantification of Finite Element models using modal statistical information
(2022)This paper develops a Hierarchical Bayesian Modeling (HBM) framework for uncertainty quantification of Finite Element (FE) models based on modal information. This framework uses an existing Fast Fourier Transform (FFT) ...