  Over the last two decades, the science has come a long way from relying on
only physical experiments and observations to experimentation using computer
simulators. This chapter focusses on the modelling and analysis of data arising
from computer simulators. It turns out that traditional statistical metamodels
are often not very useful for analyzing such datasets. For deterministic
computer simulators, the realizations of Gaussian Process (GP) models are
commonly used for fitting a surrogate statistical metamodel of the simulator
output. The chapter starts with a quick review of the standard GP based
statistical surrogate model. The chapter also emphasizes on the numerical
instability due to near-singularity of the spatial correlation structure in the
GP model fitting process. The authors also present a few generalizations of the
GP model, reviews methods and algorithms specifically developed for analyzing
big data obtained from computer model runs, and reviews the popular analysis
goals of such computer experiments. A few real-life computer simulators are
also briefly outlined here.
