Prediction and Computer Model Calibration Using Outputs From Multifidelity Simulators

• Published in: Technometrics Vol. 55; no. 4; pp. 501 - 512
• Main Authors: Goh, Joslin, Bingham, Derek, Holloway, James Paul, Grosskopf, Michael J., Kuranz, Carolyn C., Rutter, Erica
• Format: Journal Article
• Language: English
• Published: Alexandria Taylor & Francis Group 01-11-2013; American Society for Quality and the American Statistical Association; American Society for Quality
• Subjects: Bayesian analysis; Calibration; Computer experiment; Computer simulation; Fluid mechanics; Gaussian process; Markov chain Monte Carlo; Sensitivity analysis; Simulators
• ISSN: 0040-1706; 1537-2723
• DOI: 10.1080/00401706.2013.838910

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.