  When modelling turbulent flows, it is often the case that information on the
forcing terms or the boundary conditions is either not available or overly
complicated and expensive to implement. Instead, some flow features, such as
the mean velocity profile or its statistical moments, may be accessible through
experiments or observations. We present a method based on physics-informed
neural networks to generate turbulent states subject to a set of given
conditions. The physics-informed method ensures the final state approximates a
valid flow. We show examples of different statistical conditions that can be
used to prepare states, motivated by experimental and atmospheric problems.
Lastly, we show two ways of scaling the resolution of the prepared states. One
is through the use of multiple and parallel neural networks. The other uses
nudging, a synchronization-based data assimilation technique that leverages the
power of specialized numerical solvers.
