> In each time step, the solver performs an "advection" update followed by a "pressure projection" step. This "pressure projection" step involves solving a "Poi

> In each time step, the solver performs an "advection" update followed by a "pressure projection" step. This "pressure projection" step involves solving a "Poisson equation," which is computationally intensive and usually requires an iterative solver. Instead of directly solving the Poisson equation, they train a ConvNet regression model to infer these "pressure projections." Yea, I mean, for applications like solving a Poisson equation, it seems like it would be cheaper to just solve the damn linear system. The system is going to be fairly sparse with a nice, well understood distribution of non-zeros and one should be able to make the linear solve fly. I'm curious where the tipping point is, however, between just solving the linear system and pushing everything through a CNN.