  In this work, we introduce an efficient generation procedure to produce
synthetic multi-modal datasets of fluid simulations. The procedure can
reproduce the dynamics of fluid flows and allows for exploring and learning
various properties of their complex behavior, from distinct perspectives and
modalities. We employ our framework to generate a set of thoughtfully designed
training datasets, which attempt to span specific fluid simulation scenarios in
a meaningful way. The properties of our contributions are demonstrated by
evaluating recently published algorithms for the neural fluid simulation and
fluid inverse rendering tasks using our benchmark datasets. Our contribution
aims to fulfill the community's need for standardized training data, fostering
more reproducibile and robust research.
