Invited SpeakersProfile Details

NILS THUEREY Assistant-Professor at the Technical University of Munich


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.

All sessions by NILS THUEREY

  • Day 1Monday, April 10th
1:30 pm

Fluid Simulations and Neural Networks

Physics simulations for virtual smoke, explosions or water are by now crucial tools for special effects. Despite their wide spread 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 outline research directions to alleviate these inherent difficulties with machine learning techniques based on neural networks. These networks can learn and represent highly non-linear functions, which turns out to be highly useful in the flow simulation context.

A central part of this talk will be devoted to methods to enable the data-driven synthesis of fluid effects. In particular, I will outline methods to synthesize smoke volumes with pre-computed libraries of data, and a method the synthesizes liquid animations by deforming space-time data-sets of liquid surfaces. In both cases convolutional neural networks play a central role to make the approaches efficient and tractable.

I will show several examples of smoke and liquid animations generated with these approaches, and explain challenges encountered during data generation and training. In addition, I will discuss limitations and future directions of this new field of research.

KAUST 13:30 - 14:00 Details