Advances in the Sequential Design of Computer Experiments Based on Active Learning

Abstract

We present some advances in the design of computer experiments. A Gaussian Process (GP) model is fitted to the computer experiment data as a surrogate model. We investigate using the Active Learning (AL) strategy of finding design points that maximize reduction on predictive variance. Using a series of approximations based on standard results from linear algebra (Weyl's inequalities), we establish a score that approximates the AL utility. Our method is illustrated with a simulated example as well as with an intermediate climate computer model.