Model Calibration Through Minimal Adjustments

Abstract

The Gaussian process (GP) model provides a powerful methodology for calibrating a computer model in the presence of model uncertainties. However, if the data contain systematic experimental errors, then the GP model can lead to an unnecessarily complex adjustment of the computer model. In this work, we introduce an adjustment procedure that brings the computer model closer to the data by making minimal changes to it. This is achieved by applying a lasso-based variable selection on the systematic experimental error terms while fitting the GP model. Two real examples and simulations are presented to demonstrate the advantages of the proposed approach. This article has supplementary material available online.

KEY WORDS:

• Computer experiments
• Gaussian process
• Prediction
• Validation
• Variable selection

ACKNOWLEDGMENTS

The authors thank the Editor, AE, and two referees for their valuable comments and suggestions. This research was supported by U.S. National Science Foundation grant CMMI-0654369.