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Prediction and Computer Model Calibration Using Outputs from Multifidelity Simulators

Summary: [This abstract is based on the authors' 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, the authors 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. The 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.

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  • Topics: Statistics
  • Keywords: Process dynamics, Calibration, Gaussian curve, Markov chains, Monte Carlo methods, Computer models, Simulations
  • Author: Goh, Joslin; Bingham, Derek; Holloway, James Paul; Grosskopf, Michael J.; Kuranz, Carolyn C.; Rutter, Erica;
  • Journal: Technometrics