Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
A multigrid tutorial (2nd ed.)
A multigrid tutorial (2nd ed.)
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Smoke simulation for large scale phenomena
ACM SIGGRAPH 2003 Papers
Animating suspended particle explosions
ACM SIGGRAPH 2003 Papers
Fluid simulation interaction techniques
ACM SIGGRAPH 2003 Sketches & Applications
ACM SIGGRAPH 2004 Papers
Directable photorealistic liquids
SCA '04 Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation
Growing up with fluid simulation on "The Day After Tomorrow"
SIGGRAPH '04 ACM SIGGRAPH 2004 Sketches
Curl-noise for procedural fluid flow
ACM SIGGRAPH 2007 papers
An Unconditionally Stable MacCormack Method
Journal of Scientific Computing
Fluid Simulation
Low viscosity flow simulations for animation
Proceedings of the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
A parallel multigrid Poisson solver for fluids simulation on large grids
Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Simulations of single and multiple swimmers with non-divergence free deforming geometries
Journal of Computational Physics
Simulating massive dust in Megamind
ACM SIGGRAPH 2011 Talks
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Spatio-temporal extrapolation for fluid animation
ACM Transactions on Graphics (TOG)
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We describe a framework for creating animated special effects such as dust, smoke and fire using a generalized transport system coupled with a fluid dynamics engine. The framework presents artists with an open system for animating volumes with generic transport properties such as diffusion rate, dissipation rate, and contribution to buoyancy and gradient forces. Artists may attach additional volumes representing source terms, external forces and collisions that control the fluid motion. By presenting all inputs to the fluid solver as volumetric data, we can take advantage of a fast, direct FFT-based elliptic solver whose performance and scalability is optimal on shared-memory multiprocessors and is dramatically faster than the best iterative methods.