BYU

Abstract by Javid Pack

Personal Infomation


Presenter's Name

Javid Pack

Co-Presenters

None

Degree Level

Masters

Co-Authors

None

Abstract Infomation


Department

Computer Science

Faculty Advisor

Parris Egbert

Title

Real-time FLIP Fluid Simulation through Machine Learning Approximations

Abstract

3M - Fluids in computer generated imagery can add an impressive amount of realism to a scene. Fluid simulations are used extensively in modern computer animated films but are noticeably lacking in interactive applications such as computer games. The reason for the lack of fluid simulations in interactive applications is due to the prohibitively expensive computations required by the computer to simulate fluids using current algorithms. Recent research efforts have attempted to utilize machine learning techniques to approximate the movement of fluids in pursuit of real-time simulations. We propose integrating machine learning prediction techniques into a Fluid-Implicit-Particle (FLIP) simulation method and expect the hybrid nature of FLIP to facilitate further improvements in speed, visual quality, and flexibility when compared to current techniques.