  We present a novel data-driven algorithm to synthesize high-resolution flow
simulations with reusable repositories of space-time flow data. In our work, we
employ a descriptor learning approach to encode the similarity between fluid
regions with differences in resolution and numerical viscosity. We use
convolutional neural networks to generate the descriptors from fluid data such
as smoke density and flow velocity. At the same time, we present a deformation
limiting patch advection method which allows us to robustly track deformable
fluid regions. With the help of this patch advection, we generate stable
space-time data sets from detailed fluids for our repositories. We can then use
our learned descriptors to quickly localize a suitable data set when running a
new simulation. This makes our approach very efficient, and resolution
independent. We will demonstrate with several examples that our method yields
volumes with very high effective resolutions, and non-dissipative small scale
details that naturally integrate into the motions of the underlying flow.
