But for real-time animation, this is very nice. The problem with particle fluid simulations is that everything scales linearly, but only in the number of operations per particle per simulation timestep. But the more particles you have, the smaller the timestep needs to be in order to not let the simulation explode. And this is a real problem, because you want multi-million particle simulations for realistic water, but then the number of steps you need to compute per frame increases.
They managed to do a single simulation step per frame.
"In our future work we want to [...] combine learning methods with standard solvers to obtain both fast and highly accurate simulations."
From the paper's conclusion.
> We designed a feature vector, directly modelling individual forces and constraints from the Navier-Stokes equations, giving the method strong generalization properties to reliably predict positions and velocities of particles in a large time step setting on yet unseen test videos.
However, we should restrict our attention to flows that we very strongly believe are correctly described by NS. From the original paper:
>> The main problem of our method is the same as the weakness of all machine learning approaches; the learning methods are not capable to extrapolate the model far outside the data observed during training.
The training data are existing numerical simulations of specific fluid flows. The learning algorithm learned fluid flow dynamics from simulations. This is extremely impressive. It also achieves a significant speed up in simulation time - which is also very impressive. However, as the authors say, it does not generalise well. So, in short the answer to your first question is probably "sometimes yes, but in general not really".
edit: I should add that the simulations in the paper are decidedly based on Navier-Stokes.
[1] https://en.wikipedia.org/wiki/Hagen%E2%80%93Poiseuille_flow_...
[2] https://en.wikipedia.org/wiki/Navier%E2%80%93Stokes_existenc...