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This paper addresses the Bayesian calibration of dynamic models withparametric and structural uncertainties, in particular where the uncertainparameters are unknown/poorly known spatio-temporally varying subsystem models.Independent stationary Gaussian processes with uncertain hyper-parametersdescribe uncertainties of the model structure and parameters whileKarhunnen-Loeve expansion is adopted to spectrally represent these Gaussianprocesses. The Karhunnen-Loeve expansion of a prior Gaussian process isprojected on a generalized Polynomial Chaos basis, whereas intrusive Galerkinprojection is utilized to calculate the associated coefficients of thesimulator output. Bayesian inference is used to update the prior probabilitydistribution of the generalized Polynomial Chaos basis, which along with thechaos expansion coefficients represent the posterior probability distribution.Parameters of the posterior distribution are identified that quantifycredibility of the simulator model. The proposed method is demonstrated forcalibration of a simulator of quasi-one-dimensional flow through a divergentnozzle.

A Generalized Polynomial Chaos-Based Method for Efficient Bayesian Calibration of Uncertain Computational Models