Calibration and improved prediction of computer models by universal Kriging

TL;DR

This paper introduces a universal Kriging-based statistical method for calibrating computer models using experimental data, significantly enhancing their predictive accuracy and uncertainty quantification for physical systems.

Contribution

It proposes a global Gaussian process approach for model calibration and prediction correction, integrating physical expertise within a Bayesian framework.

Findings

Improved predictions of the thermal-hydraulic code FLICA 4.

Quantified uncertainty in model predictions.

Enhanced calibration accuracy using experimental data.

Abstract

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.