Calibration and Uncertainty Analysis for Computer Simulations with Multivariate Output

摘要

Model calibration analysis is concerned with the estimation of unobservable modeling parameters using observations of system response. When the model being calibrated is an expensive computer simulation, special techniques such as surrogate modeling and Bayesian inference are often fruitful. In this paper, we show how the flexibility of the Bayesian calibration approach can be exploited to account for a wide variety of uncertainty sources in the calibration process. We propose a straightforward approach for simultaneously handling Gaussian and non-Gaussian errors, as well as a framework for studying the effects of prescribed uncertainty distributions for model inputs that are not treated as calibration parameters. Further, we discuss how Gaussian process surrogate models can be used effectively when simulator response may be a function of time and/or space (multivariate output). The proposed methods are illustrated through the calibration of a simulation of thermally decomposing foam.