Prediction and Computer Model Calibration Using Outputs From Multifidelity Simulators

Abstract

Abstract Computer simulators are widely used to describe and explore physical processes. In some cases, several simulators are available, each with a different degree of fidelity, for this task. In this work, we combine field observations and model runs from deterministic multifidelity computer simulators to build a predictive model for the real process. The resulting model can be used to perform sensitivity analysis for the system, solve inverse problems, and make predictions. Our approach is Bayesian and is illustrated through a simple example, as well as a real application in predictive science at the Center for Radiative Shock Hydrodynamics at the University of Michigan. The Matlab code that is used for the analyses is available from the online supplementary materials. KEY WORDS: Computer experimentGaussian processMarkov chain Monte Carlo ACKNOWLEDGMENTS This work is funded by the Predictive Sciences Academic Alliances Program in NNSA-ASC via grant DEFC52-08NA28616 and the Natural Sciences and Engineering Research Council of Canada. The authors are grateful for the encouraging and helpful comments made by the referees, Associate Editor, and Editor. The implementation of the proposed methodology is built upon the Gaussian Process Modeling for Simulation Analysis software (Gattiker, Higdon, and Williams Citation2008) developed at Los Alamos National Laboratory. The authors would like to thank the Los Alamos National Laboratory Statistical Sciences Group for sharing their libraries.