  Simulations of turbulent flows in 3D are one of the most expensive
simulations in computational fluid dynamics (CFD). Many works have been written
on surrogate models to replace numerical solvers for fluid flows with faster,
learned, autoregressive models. However, the intricacies of turbulence in three
dimensions necessitate training these models with very small time steps, while
generating realistic flow states requires either long roll-outs with many steps
and significant error accumulation or starting from a known, realistic flow
state - something we aimed to avoid in the first place. Instead, we propose to
approach turbulent flow simulation as a generative task directly learning the
manifold of all possible turbulent flow states without relying on any initial
flow state. For our experiments, we introduce a challenging 3D turbulence
dataset of high-resolution flows and detailed vortex structures caused by
various objects and derive two novel sample evaluation metrics for turbulent
flows. On this dataset, we show that our generative model captures the
distribution of turbulent flows caused by unseen objects and generates
high-quality, realistic samples amenable for downstream applications without
access to any initial state.
