  With the growing number of wind farms over the last decades and the
availability of large datasets, research in wind-farm flow modeling - one of
the key components in optimizing the design and operation of wind farms - is
shifting towards data-driven techniques. However, given that most current
data-driven algorithms have been developed for canonical problems, the enormous
complexity of fluid flows in real wind farms poses unique challenges for
data-driven flow modeling. These include the high-dimensional multiscale nature
of turbulence at high Reynolds numbers, geophysical and atmospheric effects,
wake-flow development, and incorporating wind-turbine characteristics and
wind-farm layouts, among others. In addition, data-driven wind-farm flow models
should ideally be interpretable and have some degree of generalizability. The
former is important to avoid a lack of trust in the models with end-users,
while the most popular strategy for the latter is to incorporate known physics
into the models. This article reviews a collection of recent studies on
wind-farm flow modeling covering both purely data-driven and physics-guided
approaches. We provide a thorough analysis of their modeling approach,
objective, and methodology, and specifically focus on the data utilized in the
reviewed works.
