  Simulating fluid dynamics is crucial for the design and development process,
ranging from simple valves to complex turbomachinery. Accurately solving the
underlying physical equations is computationally expensive. Therefore,
learning-based solvers that model interactions on meshes have gained interest
due to their promising speed-ups. However, it is unknown to what extent these
models truly understand the underlying physical principles and can generalize
rather than interpolate. Generalization is a key requirement for a
general-purpose fluid simulator, which should adapt to different topologies,
resolutions, or thermodynamic ranges. We propose SURF, a benchmark designed to
test the $\textit{generalization}$ of learned graph-based fluid simulators.
SURF comprises individual datasets and provides specific performance and
generalization metrics for evaluating and comparing different models. We
empirically demonstrate the applicability of SURF by thoroughly investigating
the two state-of-the-art graph-based models, yielding new insights into their
generalization.
