Fluid control using the adjoint method

By A. McNamara, Adrien Treuille, Zoran Popovic, J. Stam. ACM Transactions on Graphics, 2004

408 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design

View Paper: https://www.semanticscholar.org/paper/41712a2ac31dfd94978e0ce2f1b5290cc59d0605

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In Computational Mechanics

In Computer Graphics and Computer-Aided Design

Citations in the corpus

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, neutral): “[285] A.”
Guiding of smoke animations through variational coupling of simulations at different resolutions (2009, methodology): “McNamara et al. [ MTPS04 ] improved the speed of this framework by several orders of magnitude by adopting the adjoint method.”
Linear Bellman combination for control of character animation (2009, methodology): “We then optimize this trajectory using an adjoint method [McNamara et al. 2004] or differential dynamic programming (DDP) [Jacobson and Mayne 1970].”
Blending liquids (2014, methodology): “Several previous works share our goal of modifying an existing fluid simulation, using non-linear optimization [McNamara et al. 2004] or forces [Shi and Yu 2005; Thuerey et al.”
Interactive localized liquid motion editing (2013, methodology): “As a viable alternative to explicitly tuning parameters and boundary conditions, some previous methods [Treuille et al. 2003; McNamara et al. 2004; Wojtan et al. 2006] proposed to specify a priori a set of global fluid shape keyframes and rely on an offline numerical optimization scheme to find the ”
Wavelet turbulence for fluid simulation (2008, methodology): “Recent work on fluid control has focused on keyframe-driven animation [McNamara et al. 2004; Fattal and Lischinski 2004] and motion filaments [Angelidis et al. 2006] that control the low-frequency flow.”
FEM simulation of 3D deformable solids: a practitioner's guide to theory, discretization and model reduction (2012, background): “Often, they are cast as space-time optimization problems involving human motion [34], fluids [40, 30] and deformations [42].”
Learning to Control PDEs with Differentiable Physics (2020, methodology): “This causes the adjoint problem to have the same mathematical form (McNamara et al., 2004) as the original problem.”
Coupling water and smoke to thin deformable and rigid shells (2005, methodology): “2004], control [McNamara et al. 2004; Mihalef et al. 2004], and octree data structures [Losasso et al.”
Fluid directed rigid body control using deep reinforcement learning (2018, methodology): “Numerous methods, such as [Fattal and Lischinski 2004; McNamara et al. 2004; Pan et al. 2013; Pan and Manocha 2017; Shi and Yu 2005; Thuerey 2016; Treuille et al. 2003], have been proposed to this end.”