  We present an introduction to some of the state of the art in reduced order
and surrogate modeling in gravitational wave (GW) science. Approaches that we
cover include Principal Component Analysis, Proper Orthogonal Decomposition,
the Reduced Basis approach, the Empirical Interpolation Method, Reduced Order
Quadratures, and Compressed Likelihood evaluations. We divide the review into
three parts: representation/compression of known data, predictive models, and
data analysis. The targeted audience is that one of practitioners in GW
science, a field in which building predictive models and data analysis tools
that are both accurate and fast to evaluate, especially when dealing with large
amounts of data and intensive computations, are necessary yet can be
challenging. As such, practical presentations and, sometimes, heuristic
approaches are here preferred over rigor when the latter is not available. This
review aims to be self-contained, within reasonable page limits, with little
previous knowledge (at the undergraduate level) requirements in mathematics,
scientific computing, and other disciplines. Emphasis is placed on optimality,
as well as the curse of dimensionality and approaches that might have the
promise of beating it. We also review most of the state of the art of GW
surrogates. Some numerical algorithms, conditioning details, scalability,
parallelization and other practical points are discussed. The approaches
presented are to large extent non-intrusive and data-driven and can therefore
be applicable to other disciplines. We close with open challenges in high
dimension surrogates, which are not unique to GW science.
