Computer simulation models are now an important tool in many fields of research and business.
Simulation models tend to be computationally intensive and, hence, expensive to run.
Many interesting studies that could be performed using simulation models are left undone because of the expense of generating observations. Finding methods to inexpensively predict the response for new inputs to the simulation model is essential if these studies are to be done. A computer experiment is a set of simulation runs with various input values. Most simulation models are deterministic, but modeling the output of the simulation runs as a realization of a stochastic process provides the basis for using statistical methods to create an inexpensive predictor. Specifically, the output of the simulation model is assumed to be a realization of a Gaussian stochastic process with a stationary covariance function.