Abstract

Finding optimal designs for computer experiments that are modeled using a stationary Gaussian Stochastic Process (GaSP) model is challenging because optimality criteria are usually functions of the unknown model parameters. One popular approach is to adopt sequential strategies. These have been shown to be very effective when the optimality criterion is formulated as an expected improvement function. Most of these sequential strategies assume observations are taken sequentially one at a time. However, when observations can be taken k at a time, it is not obvious how to implement sequential designs. We discuss the problems that can arise when implementing batch sequential designs and present several strategies for sequential designs taking observations in k-at-a-time batches. We illustrate these strategies with examples.