Computer Graphics Laboratory ETH Zurich


Data-driven Fluid Simulations using Regression Forests

L. Ladicky, S. Jeong, B. Solenthaler, M. Pollefeys, M. Gross

Proceedings of ACM SIGGRAPH Asia (Kobe, Japan, 2-5 November, 2015), ACM Transactions on Graphics, vol. 34, no. 6, pp. 199:1--199:9
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Traditional fluid simulations require large computational resources even for an average sized scene with the main bottleneck being a very small time step size, required to guarantee the stability of the solution. Despite a large progress in parallel computing and efficient algorithms for pressure computation in the recent years, realtime fluid simulations have been possible only under very restricted conditions. In this paper we propose a novel machine learning based approach, that formulates physics-based fluid simulation as a regression problem, estimating the acceleration of every particle for each frame. We designed a feature vector, directly modelling individual forces and constraints from the Navier-Stokes equations, giving the method strong generalization properties to reliably predict positions and velocities of particles in a large time step setting on yet unseen test videos. We used a regression forest to approximate the behaviour of particles observed in the large training set of simulations obtained using a traditional solver. Our GPU implementation led to a speed-up of one to three orders of magnitude compared to the state-of-the-art position-based fluid solver and runs in real-time for systems with up to 2 million particles.

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author = {Ladick\'{y}, L'ubor and Jeong, SoHyeon and Solenthaler, Barbara and Pollefeys, Marc and Gross, Markus},
title = {Data-driven Fluid Simulations Using Regression Forests},
journal = {ACM Trans. Graph.},
issue_date = {November 2015},
volume = {34},
number = {6},
month = oct,
year = {2015},
issn = {0730-0301},
pages = {199:1--199:9},
articleno = {199},
numpages = {9},
url = {},
doi = {10.1145/2816795.2818129},
acmid = {2818129},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {data-driven, fluid simulation, regression forest},
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Lubor Ladicky, SoHyeon Jeong, Barbara Solenthaler, Marc Pollefeys, Markus Gross


Figure 1 : The obtained results using our regression forest method, capable of simulating millions of particles in realtime. Our promising results suggest the applicability of machine learning techniques to physics-based simulations in time-critical settings, where running time matters more than the physical exactness.


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