Browsing by Subject "Uncertainty quantification"
Now showing items 1-4 of 4
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Bayesian uncertainty quantification and propagation for discrete element simulations of granular materials
(2014)Predictions in the behavior of granular materials using Discrete Element Methods (DEM) hinge on the employed interaction potentials. Here we introduce a data driven, Bayesian framework to quantify DEM predictions. Our ... -
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 ... -
Pi 4U: A high performance computing framework for Bayesian uncertainty quantification of complex models
(2015)We present Pi 4U,(1) an extensible framework, for non-intrusive Bayesian Uncertainty Quantification and Propagation (UQ+P) of complex and computationally demanding physical models, that can exploit massively parallel ...