Date: | Jan 7, 2015 |
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Location: | DC 1331 |
Time: | 11:30 |
Chair: | Christopher Batty |
Date: | Jan 14, 2015 | Jan 21, 2015 | Jan 28, 2015 | Feb 4, 2016 |
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Location: | DC 1331 11:30 | DC 1331 11:30 | DC 1331 11:30 | DC 1331 11:30 |
Chair: | Bill Cowan |
Ryan Goldade |
Craig Kaplan |
Marta Kryven |
Technical Presentation: | Christopher Batty |
Bill Cowan |
Ryan Goldade |
Craig Kaplan |
Yipeng Wang (maybe) |
Title : Data-driven fluid simulations using regression forests
Abstract: 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, real time fluid simulations have been possible only under very restricted conditions. In this paper the authors 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. They 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.
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Also see other Math and CS postings.