Optimum Design for Correlated Fields via Covariance Kernel Expansions

In this paper we consider optimal design of experiments for correlated observations. We approximate the error component of the process by an eigenvector expansion of the corresponding covariance function. Furthermore we study the limiting behavior of an additional white noise as a regularization tool. The approach is illustrated by some typical examples.