Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: 
This paper proposes a specially tailored convolutional neural network for the efficient simulation of incompressible fluid flow, achieving significant speedup compared to classic simulator and more realistic outputs compared to other data-driven approach. I think the use of objective function that “emphasizes the divergence of voxels on geometry boundaries” deserves more explanation and investigation. In addition, while the qualitative comparison of different methods in the appendix is intuitive and convincing, I think it will be very helpful to develop some quantitative evaluation metrics based on fluid dynamics theories.

review_title: null

final_score: rating: 7: Good paper, accept
review_content: I am not an expert on this domain, but the problem tackled sounds interesting and the presentation of the proposed method is clear. The presented results show good speedups compared to the baseline.
I am curious to know what the approximation effect on the final simulation is? In particular, how much does this approximation affect the final simulation compared to when an exact solver or other approximation techniques are used? Is there a metric that measures this approximation effect?
I did not understand what is hybrid about the proposed method? This is mentioned in Section 1, Page 2, last paragraph.
review_title: This paper provides an efficient method for approximating the solution of the Euler Equations, which has applications in 2D and 3D simulation of fluids.

Decision: Accept