Sfoglia per Soggetto "Asymptotic approximation"
Items 1-13 di 13
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Bayesian finite element model updating
(2019)In this chapter, the implementation of the reduced-order models within Bayesian finite element model updating is explored. The Bayesian framework for model parameter estimation, model selection, and robust predictions of ... -
Bayesian methodology for structural damage identification and reliability assessment
(2009)A Bayesian framework is presented for structural model selection and damage identification utilizing measured vibration data. The framework consists of a two-level approach. At the first level the problem of estimating 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 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 ... -
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 physics-based models of systems, as well as propagating these uncertainties to estimate the ... -
Efficient techniques for bayesian inverse modeling of large-order computational models
(2013)Bayesian tools for inverse modeling are based on asymptotic approximations and Stochastic Simulation Algorithms (SSA). Such tools require a number of moderate to large number of system re-analyses. For large-order numerical ... -
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 ... -
Fast Bayesian structural damage localization and quantification using high fidelity FE models and CMS techniques
(2012)Bayesian estimators are proposed for damage identification (localization and quantification) of civil infrastructure using vibration measurements. The actual damage occurring in the structure is predicted by Bayesian model ... -
A hierarchical Bayesian framework for force field selection in molecular dynamics simulations
(2016)We present a hierarchical Bayesian framework for the selection of force fields in molecular dynamics (MD) simulations. The framework associates the variability of the optimal parameters of the MD potentials under different ... -
An information theoretic framework for optimal experimental design
(2017)An information theoretic framework for optimal experimental design is presented. The objective function is rooted in information theory, and is the expected Kullback-Leibler divergence between the prior and posterior pdf ... -
Robust Bayesian optimal sensor placement for model parameter estimation and response predictions
(2020)Optimal sensor placement (OSP) in complex systems implies a configuration that maximizes the information gain by the sensors. This configuration is identified by maximizing, with respect to the location of the sensors, an ... -
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 ... -
Treatment of unidentifiability in structural model updating
(2000)The present study addresses the issues of non-uniqueness and unidentifiability arising in structural model updating. A Bayesian probabilistic frame-work is used for model updating which properly handles the uncertainties ...