Model reduction for real-time fluids

By Adrien Treuille, Andrew Lewis, Zoran Popovic. 2006

195 citations recorded by the source corpus.

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Citations in the corpus

FEM simulation of 3D deformable solids: a practitioner's guide to theory, discretization and model reduction (2012, methodology): “Treuille and colleagues applied model reduction to fluid simulation in computer graphics [39].”
To appear in the ACM SIGGRAPH conference proceedings Modular Bases for Fluid Dynamics (unknown): “As demonstrated in [Treuille et al. 2006], the Navier Stokes equations can be reduced to”
Data‐driven projection method in fluid simulation (2016, background): “In ﬂuid simulation, the most common idea is to transform the solving process into low-dimensional space [13,14].”
Adaptively sampled particle fluids (2007, methodology): “2006], model reduction techniques [Treuille et al. 2006] and dynamic non-uniform mesh refinement [Klingner et al.”
Data-driven fluid simulations using regression forests (2015, methodology): “The most common data-driven approaches for fluid simulations are based on Galerkin projection [Treuille et al. 2006; Gupta and Narasimhan 2007; De Witt et al. 2012], that transforms the dynamics of the fluid simulation to operations on linear combinations of preprocessed snap-shots (tiles).”
Learning to Control PDEs with Differentiable Physics (2020, methodology): “We believe that this is a good example of leveraging the methodology of adjoint method optimizations (Giles & Pierce, 2000; Treuille et al., 2006; Pontryagin, 1962) within deep learning.”
Interactive localized liquid motion editing (2013, methodology): “Previous fluid model reduction methods [Treuille et al. 2006] extracting a reduced bases from training examples require a large memory footprint due to the lack of analytical representations.”