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Sequential calibration of computer models

Kumar, A (Ohio State University)
Friday 15 August 2008, 14:30-15:00

Seminar Room 1, Newton Institute


We propose a sequential method for the estimation of calibration parameters for computer models. The goal is to find the values of the calibration parameters that bring a computer simulation into ``best'' agreement with data from a physical experiment. In this method, we first fit separate Gaussian Stochastic Process(GASP) models to given data from a physical and a computer experiment. The values of the calibration parameters that minimize the discrepancy between predictions from the two models, are taken as the estimates. In the second step, the point with maximum potential for reducing the uncertainty in the fitted model is identified. The Computer experiment is conducted at this new point. The first step is repeated with the augmented data set, the calibration parameters re-estimated, and the next design point determined. The method is repeated until the allocated budget for the number of design points are exhausted or the calibration parameters' estimates are satisfactory. Empirical results shows effectiveness of the sequential procedure in achieving faster convergence to the estimates of the calibration parameters when a unique best estimate exists.


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