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Computer experiments have become ubiquitous in science and engineering.Commonly, runs of these simulations demand considerable time and computing,making experimental design extremely important in gaining high qualityinformation with limited time and resources. Principles of experimental designare proposed and justified which ensure high nominal, numeric, and parameterestimation accuracy for Gaussian process emulation of deterministicsimulations. The space-filling properties "small fill distance" and "largeseparation distance" are only weakly conflicting and ensure well-controllednominal, numeric, and parameter estimation error, while non-stationarityrequires a greater density of experimental inputs in regions of the input spacewith more quickly decaying correlation. This work will provide scientists andengineers with robust, rigorously justified, and practically useful overarchingprinciples for selecting combinations of simulation inputs with highinformation content.

A Framework for Controlling Sources of Inaccuracy in Gaussian Process Emulation of Deterministic Computer Experiments