Toward Real-Time FLIP Fluid Simulation through Machine Learning Approximations
Fluids in computer generated imagery can add an impressive amount of realism to a scene, but are particularly time-consuming to simulate. In an attempt to run fluid simulations in real-time, recent efforts have attempted to simulate fluids by using machine learning techniques to approximate the movemen...
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| Format: | Others |
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BYU ScholarsArchive
2018
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| Online Access: | https://scholarsarchive.byu.edu/etd/8828 https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=9837&context=etd |
| Summary: | Fluids in computer generated imagery can add an impressive amount of realism to a scene, but are particularly time-consuming to simulate. In an attempt to run fluid simulations in real-time, recent efforts have attempted to simulate fluids by using machine learning techniques to approximate the movement of fluids. We explore utilizing machine learning to simulate fluids while also integrating the Fluid-Implicit-Particle (FLIP) simulation method into machine learning fluid simulation approaches. |
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