Accelerating Eulerian Fluid Simulation With Convolutional Networks

Jonathan Tompson from Google and his colleagues, Kristofer Schlachter, Pablo Sprechmann and Ken Perlin from the New York University have come up with a really nice technique to teach a convolutional neural network how fluid and smoke simulations work. The project webpage is available here, or you can click on the image below to access the paper.

Status: in progress or under review

(please note that I am in the acknowledgements section and was not a co-author of the paper)


Surface-Only Liquids

Fang Da and his colleagues published an absolutely amazing paper on fluid simulations. Make sure to have a look, it is available here (or click on the paper below). I helped a bit with setting up the rendering pipeline and the scenes properly.

Status: accepted to ACM SIGGRAPH 2016

(please note that I am in the acknowledgements section and was not a co-author of the paper)




Two Minute Papers – Creating Stunning Fluid Simulations with Wavelet Turbulence

A quick two minute explanation of one of the greatest fluid papers ever written: the Academy Award-winning Wavelet Turbulence.

Creating detailed fluid and smoke simulations in Blender and other modeling software is a slow and laborious process that requires a ton of time and resources. Wavelet Turbulence is a technique that helps achieving similar effects orders of magnitude faster. It is also much lighter on memory and is now widely used in the industry, so it’s definitely not an accident that Theodore Kim won an Academy Award (a technical Oscar, if you will) for this SIGGRAPH publication. It is implemented in Blender and is available for everyone free of charge, so make sure to try it out! The paper is available here.

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