Physics simulations for virtual smoke, explosions or water are by now crucial tools for special
effects. Despite their widespread use, it is still difficult to get get these simulations under
control, and they are still far too expensive for practical interactive applications.
In this talk I will discuss using deep learning for physics problems, and outline research
directions to alleviate the inherent difficulties of fluids simulations with the help of deep
convolutional neural networks. As powerful tools to approximate complex nonlinear functions these
networks can enable new directions of working with fluid simulations. I will show several recent
examples of how fluid simulations and neural networks can work together, e.g., to synthesize new
simulations with pre-computed patches of flow data, or to enable interactive liquids applications
by warping space-time surfaces.
In the end, I will also discuss possible future directions for this area. It's of course hard to
predict how well deep learning will do in the long run, but fluids (as placeholders for complex
physics phenomena in general) are posing very interesting research challenges for deep learning
Nils Thuerey is an Assistant-Professor at the Technical University of Munich (TUM). He works in
the field of computer graphics, with a particular emphasis on physically-based animation. One focus
area of his research targets the simulation of fluid phenomena, such as water and smoke. These
simulations find applications as visual effects in computer generated movies and digital games.
Examples of his work are novel algorithms to make simulations easier to control, to handle detailed
surface tension effects, and to increase the amount of turbulent detail.
After studying computer science, Nils Thuerey acquired a PhD for his work on liquid simulations
in 2006. He received both degrees from the University of Erlangen-Nuremberg. Until 2010 he held a
position as a post-doctoral researcher at ETH Zurich. He received a tech-Oscar from the AMPAS in
2013 for his research on controllable smoke effects. Subsequently, he worked for three years as
R&D lead at ScanlineVFX, before he started at TUM in October 2013.
More information on
University of Stuttgart, VISUS-Building, Allmandring 19, Vaihingen
Powerwall Room -01.116
University of Konstanz, Universitätsstr. 10, Konstanz
Powerwall C202 (Live Transmission)