Paper:
Recent studies at the intersection of physics and deep learning have illustrated successes in the application of deep neural networks to partially or fully replace costly physics simulations. Enforcing physical constraints to solutions generated by neural networks remains a challenge, yet it is essential to the accuracy and trustworthiness of such model predictions. Many systems in the physical sciences are governed by Partial Differential Equations (PDEs). Enforcing these as hard constraints, we show, are inefficient in conventional frameworks due to the high dimensionality of the generated fields. To this end, we propose the use of a novel differentiable spectral projection layer for neural networks that efficiently enforces spatial PDE constraints using spectral methods, yet is fully differentiable, allowing for its use as a layer in neural networks that supports end-to-end training. We show that its computational cost is cheaper than a regular convolution layer. We apply it to an important class of physical systems -incompressible turbulent flows, where the divergence-free PDE constraint is required. We train a 3D Conditional Generative Adversarial Network (CGAN) for turbulent flow superresolution efficiently, whilst guaranteeing the spatial PDE constraint of zero divergence. Furthermore, our empirical results show that the model produces realistic flow fields with more accurate flow statistics when trained with hard constraints imposed via the proposed novel differentiable spectral projection layer, as compared to soft constrained and unconstrained counterparts.

Review:
The paper describes a way to efficiently enforce physical constraints expressed by linear PDEs on the output of a neural network. The idea is to have, as a last layer of the network, a projection onto the constrained solution space, and to back-propagate through it. That projection layer is made efficient for high-dimensional outputs via the fast Fourier transform (FFT), exploiting a well-known numerical trick. Importantly, the proposed strategy is very general, and can indeed be used with any PDE constraint that is a linear combination of differential operators.
First, I would like to mention to the AC that the authors apparently forgot to run bibtex. The submitted paper had no bibliography and all references are "?". So technically the original submission was incomplete and would probably have to be rejected, on the grounds that it was impossible to check whether the literature references are appropriate. The authors have rectified this through a comment pointing to an (anonymous) version with references. For me, this is ok.
The research direction of the paper is a hot topic: how to reconcile data-driven deep learning with analytic physical models is, arguably, one of the big research questions holding back the wide-spread use of deep networks in several natural sciences (e.g., environmental and climate science, hydrology, etc.). Work in this direction could have a significant impact, and the approach developed the paper is rather general and, as far as I can tell, correct.
The experiments use the rather challenging task of super-resolving turbulent flow, subject  to the Navier-Stokes constraints. Experiments are run on synthetic data from the JHTD simulation dataset. This is hard to avoid, since dense ground truth for flow fields is impossible to obtain, nevertheless it  would have been more convincing to also show at least qualitative results for a real flow dataset. It is also not completely satisfactory that the experiments must use even as OUTput a significantly downscaled version of the dataset with a resolution that would not be tremendously useful in practice - this implicitly acknowledges that the paper, while reducing the computational cost compared to the direct projection method, did actually not overcome the most critical bottleneck one faces when combining high-dimensional physics simulation with deep learning: namely, that it is not tractable to simply store physical fields as explicit 3D/4D voxel grids if one wants to work with them on the GPU.
Another slightly bothering choice in the experiments is to compare only distributions. While I see the point that the prediction is, by its nature, ambiguous; I still think one should look at both flow statistics and at the actual flow field difference. Ambiguity is not a very convincing justification to not even try to predict correctly - by the author's definition any super-resolution task is ambiguous, still one aims, for instance, to recover the correct image, not just a plausible one. In a sense it is the whole point of prior knowledge (which the PDEs are in a learning context) to bring the solution closer to the right answer, when the data alone cannot do the job. In practice, a prediction that is very close to the true flow field, but ever so slightly violates the physical constraints is often more useful than one that strictly satisfies the constraints, but is way off.
One comment on the presentation: I feel that the discussion of soft constraints could be more extensive and more balanced. I agree that they are less principled, they do not devalue the present work. But we know, both for variational methods and for pre-deep learning methods, that soft constraints work rather well in practice, especially with an adaptive weight that gradually tightens the constraints. So it would be in order to not just dismiss the alternative in a half-sentence, but to give it proper consideration - especially since in terms of dissipation, it actually performs better than the hard constraint in the experiments.
Overall, I find the topic important and the presented work is a sensible and nicely generic step forward. On the negative side, the paper does not fully deliver on the promise to make physics constraints in deep networks usable in practice. My rating reflects my impression that the bibliography and references are probably correct - this should be checked before reaching a final decision.