Sfoglia per Soggetto "Uncertainty quantifications"
Items 1-20 di 33
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Approximate Bayesian computation for granular and molecular dynamics simulations
(2016)The effective integration of models with data through Bayesian uncertainty quantification hinges on the formulation of a suitable likelihood function. In many cases such a likelihood may not be readily available or it may ... -
Bayesian annealed sequential importance sampling: An unbiased version of transitional Markov chain Monte Carlo
(2018)The transitional Markov chain Monte Carlo (TMCMC) is one of the efficient algorithms for performing Markov chain Monte Carlo (MCMC) in the context of Bayesian uncertainty quantification in parallel computing architectures. ... -
Bayesian damage characterization based on probabilistic model of scattering coefficients and hybrid wave finite element model scheme
(2019)Ultrasonic Guided Wave(GW) has been proven to be sensitive to small damage. Motivated by the fact that the quantitative relationship between wave scattering and damage intensity can be described by scattering properties, ... -
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 ... -
A Bayesian framework for calibration of multiaxial fatigue curves
(2022)A Bayesian framework is proposed to re-formulate a multiaxial fatigue model and produce probabilistic stress-life fatigue curves from experimental data. The proposed framework identifies the experimentally-driven parameters ... -
Bayesian Hierarchical Models for Uncertainty Quantification in Structural Dynamics
(2014)The Bayesian framework for hierarchical modeling is applied to quantify uncertainties, arising mainly due to manufacturing variability, for a group of identical structural components. Parameterized Gaussian models of ... -
Bayesian identification of the tendon fascicle's structural composition using finite element models for helical geometries
(2017)Despite extensive experimental and computational investigations, the accurate determination of the structural composition of biological tendons remains elusive. Here we infer the structural compositions of tendons by ... -
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 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 for machine-learned models in physics
(2022)Being able to quantify uncertainty when comparing a theoretical or computational model to observations is critical to conducting a sound scientific investigation. With the rise of data-driven modelling, understanding various ... -
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 ... -
Data features-based likelihood-informed Bayesian finite element model updating
(2019)A new formulation for likelihood-informed 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 ... -
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 ... -
A fast Bayesian inference scheme for identification of local structural properties of layered composites based on wave and finite element-assisted 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, ... -
A fast CMS technique for computational efficient system re-analyses in structural dynamics
(2009)Sensitivity analyses, model calibration techniques, uncertainty quantification methods, reliability computations and design optimization methods require a moderate to large number of system re-analyses to be performed for ... -
A fast CMS technique for computational efficient system re-analyses in structural dynamics
(2013)Sensitivity analyses, model calibration techniques, uncertainty quantification methods, reliability computations and design optimization methods require a moderate to large number of system re-analyses to be performed for ... -
Fast computing techniques for Bayesian uncertainty quantification in structural dynamics
(2013)A Bayesian probabilistic framework for uncertainty quantification and propagation in structural dynamics is reviewed. Fast computing techniques are integrated with the Bayesian framework to efficiently handle large-order ... -
Fatigue monitoring and remaining lifetime prognosis using operational vibration measurements
(2019)A framework is presented for real-time monitoring of fatigue damage accumulation and prognosis of the remaining lifetime at hotspot locations of new or existing structures by combining output-only vibration measurements ... -
Hierarchical Bayesian learning framework for multi-level modeling using multi-level data
(2022)A hierarchical Bayesian learning framework is proposed to account for multi-level modeling in structural dynamics. In multi-level modeling the system is considered as a hierarchy of lower-level models, starting at the ...