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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.<\/jats:p>","DOI":"10.1145\/2816795.2818129","type":"journal-article","created":{"date-parts":[[2015,10,27]],"date-time":"2015-10-27T12:36:39Z","timestamp":1445949399000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":168,"title":["Data-driven fluid simulations using regression forests"],"prefix":"10.1145","volume":"34","author":[{"given":"L'ubor","family":"Ladick\u00fd","sequence":"first","affiliation":[{"name":"ETH Zurich"}]},{"given":"SoHyeon","family":"Jeong","sequence":"additional","affiliation":[{"name":"ETH Zurich"}]},{"given":"Barbara","family":"Solenthaler","sequence":"additional","affiliation":[{"name":"ETH Zurich"}]},{"given":"Marc","family":"Pollefeys","sequence":"additional","affiliation":[{"name":"ETH Zurich"}]},{"given":"Markus","family":"Gross","sequence":"additional","affiliation":[{"name":"ETH Zurich"}]}],"member":"320","published-online":{"date-parts":[[2015,11,2]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_2_2_1","volume-title":"Proceedings of the ACM SIGGRAPH\/Eurographics Symposium on Computer Animation, 197--206","author":"Chentanez N.","unstructured":"Chentanez , N. , and M\u00fcller , M . 2010. Real-time simulation of large bodies of water with small scale details . In Proceedings of the ACM SIGGRAPH\/Eurographics Symposium on Computer Animation, 197--206 . Chentanez, N., and M\u00fcller, M. 2010. Real-time simulation of large bodies of water with small scale details. In Proceedings of the ACM SIGGRAPH\/Eurographics Symposium on Computer Animation, 197--206."},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2010324.1964977"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2077341.2077351"},{"key":"e_1_2_2_5_1","volume-title":"-P","author":"Desbrun M.","year":"1996","unstructured":"Desbrun , M. , and Cani , M . -P . 1996 . Smoothed particles: A new paradigm for animating highly deformable bodies. In Eurographics Workshop on Computer Animation and Simulation (EGCAS), Springer-Verlag , 61--76. Desbrun, M., and Cani, M.-P. 1996. Smoothed particles: A new paradigm for animating highly deformable bodies. 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