  Physically plausible fluid simulations play an important role in modern
computer graphics and engineering. However, in order to achieve real-time
performance, computational speed needs to be traded-off with physical accuracy.
Surrogate fluid models based on neural networks have the potential to achieve
both, fast fluid simulations and high physical accuracy. However, these
approaches rely on massive amounts of training data, require complex pipelines
for training and inference or do not generalize to new fluid domains.
  In this work, we present significant extensions to a recently proposed deep
learning framework, which addresses the aforementioned challenges in 2D. We go
from 2D to 3D and propose an efficient architecture to cope with the high
demands of 3D grids in terms of memory and computational complexity.
Furthermore, we condition the neural fluid model on additional information
about the fluid's viscosity and density which allows simulating laminar as well
as turbulent flows based on the same surrogate model.
  Our method allows to train fluid models without requiring fluid simulation
data beforehand. Inference is fast and simple, as the fluid model directly maps
a fluid state and boundary conditions at a moment t to a subsequent fluid state
at t+dt. We obtain real-time fluid simulations on a 128x64x64 grid that include
various fluid phenomena such as the Magnus effect or Karman vortex streets and
generalize to domain geometries not considered during training. Our method
indicates strong improvements in terms of accuracy, speed and generalization
capabilities over current 3D NN-based fluid models.
