45,339 research outputs found
Calibration and improved prediction of computer models by universal Kriging
This paper addresses the use of experimental data for calibrating a computer
model and improving its predictions of the underlying physical system. A global
statistical approach is proposed in which the bias between the computer model
and the physical system is modeled as a realization of a Gaussian process. The
application of classical statistical inference to this statistical model yields
a rigorous method for calibrating the computer model and for adding to its
predictions a statistical correction based on experimental data. This
statistical correction can substantially improve the calibrated computer model
for predicting the physical system on new experimental conditions. Furthermore,
a quantification of the uncertainty of this prediction is provided. Physical
expertise on the calibration parameters can also be taken into account in a
Bayesian framework. Finally, the method is applied to the thermal-hydraulic
code FLICA 4, in a single phase friction model framework. It allows to improve
the predictions of the thermal-hydraulic code FLICA 4 significantly
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