Fluid dynamicist here. I wasn't aware of the machine learning work you've mentioned. They're addressing an important problem, but I'm disappointed in the paper based on a brief look. I see no comparison against experimental data (validation). Indeed, the cases the authors compute most likely have no corresponding experiments, but do look cool. I'd recommend that the authors learn more about verification, validation, and uncertainty quantification for fluid dynamics. And I'd encourage all machine learning folks approaching CFD to present your work at both fluids and machine learning conferences. I think problems like this would be reduced or fixed entirely by more interactions with fluid dynamicists.
Another smaller recommendation. What they call a "MAC grid" is called a staggered grid or mesh by CFD folks in my experience, so it might be better to use this terminology instead. The reference they cite is also out of date. I'd recommend something like this instead: https://www.sciencedirect.com/science/article/pii/S002199919...
This newer paper has higher order extensions of the method the 1965 paper uses and as I recall goes into much more detail about the properties of the schemes.
There are many recent works originating from the CFD community now. Too many to list here, you can PM me if you don't find them easily online for some reason.
Another smaller recommendation. What they call a "MAC grid" is called a staggered grid or mesh by CFD folks in my experience, so it might be better to use this terminology instead. The reference they cite is also out of date. I'd recommend something like this instead: https://www.sciencedirect.com/science/article/pii/S002199919...
This newer paper has higher order extensions of the method the 1965 paper uses and as I recall goes into much more detail about the properties of the schemes.