Comparison of Sequential Designs of Computer Experiments in High Dimensions

Abstract

We continue a long line of research in applying the design and analysis of computer experiments to the study of real world systems. The problem we consider is that of fitting a Gaussian process model for a computer model in applications where the simulation output is a function of a high dimensional input vector. Our computer experiments are designed sequentially as we learn about the model. We perform an empirical comparison of the effectiveness and efficiency of several statistical criteria that have been used in sequential experimental designs. The specific application that motivates this work comes from climatology.