  We introduce statistical techniques required to handle complex computer
models with potential applications to astronomy. Computer experiments play a
critical role in almost all fields of scientific research and engineering.
These computer experiments, or simulators, are often computationally expensive,
leading to the use of emulators for rapidly approximating the outcome of the
experiment. Gaussian process models, also known as Kriging, are the most common
choice of emulator. While emulators offer significant improvements in
computation over computer simulators, they require a selection of inputs along
with the corresponding outputs of the computer experiment to function well.
Thus, it is important to select inputs judiciously for the full computer
simulation to construct an accurate emulator. Space-filling designs are
efficient when the general response surface of the outcome is unknown, and thus
they are a popular choice when selecting simulator inputs for building an
emulator. In this tutorial we discuss how to construct these space filling
designs, perform the subsequent fitting of the Gaussian process surrogates, and
briefly indicate their potential applications to astronomy research.
