Animating sand as a fluid

By Yongning Zhu, Robert Bridson. 2005

785 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Nils Thürey
2. Markus H. Gross
3. Leonidas J. Guibas
4. Andrew Selle
5. Mark Pauly
6. Steffen Wiewel
7. Moritz Becher
8. Richard Keiser

In Computer Graphics and Computer-Aided Design

1. Nils Thürey
2. Markus H. Gross
3. Steffen Wiewel
4. Moritz Becher
5. Leonidas J. Guibas
6. Mark Pauly
7. Andrew Selle
8. Richard Keiser

In Control and Systems Engineering

1. Nils Thürey
2. Andrew Selle
3. Steffen Wiewel
4. Moritz Becher
5. Barbara Solenthaler
6. Theodore Kim
7. D. Levin
8. Yun Teng

Citations in the corpus

• Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow (2018, methodology): “Since then, the fluid implicit particle (FLIP) method has been especially popular for detailed liquid simulations [ZB05], and we will use it to generate our training data.”
• Blending liquids (2014, methodology): “2003; Solenthaler and Pajarola 2009] and hybrid approaches [Zhu and Bridson 2005].”
• Data-driven fluid simulations using regression forests (2015, methodology): “At the 2 × 2 × 2 subsampling (used for example also in the FLIP method [Zhu and Bridson 2005]) we did not observe any additional approximation artifacts.”
• The material point method for simulating continuum materials (2016, methodology): “While FLIP has been known in graphics community as a useful liquid simulation method for a while [Zhu and Bridson, 2005; Bridson, 2008; Ando and Tsuruno, 2011], MPM is only introduced and studied recently.”
• Adaptively sampled particle fluids (2007, background): “Figure 5: Comparison between our surface definition (blue curve), the definition of [Zhu and Bridson 2005] (green curve) and blobbies (red curve).”
• The affine particle-in-cell method (2015, methodology): “The exact form of those steps may be different with each phenomenon, and one can see examples of a canonical fluid loop in [Zhu and Bridson 2005] or MPM loop in [Stomakhin et al.”
• Aggregate dynamics for dense crowd simulation (2009, neutral): “CR Categories: I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism—Animation; I.6.8 [Simulation and Modeling]: Types of Simulation—Animation Keywords: crowds, planning, continuum, incompressibility”
• Eulerian solid-fluid coupling (2016, methodology): “Like the Eulerian grid projection stage of the Lagrangian FLIP method [Zhu and Bridson 2005], or the semiLagrangian particle traces of grid-based Stable Fluids [Stam 1999], we leverage the advantages of the other coordinate system during time integration without fully commiting to the representation”
• Interactive localized liquid motion editing (2013, methodology): “In this paper, our fluid simulator is based on FLIP method [Zhu and Bridson 2005], since its concept of fluid particles eases the formulation of our control problems.”
• Fluid directed rigid body control using deep reinforcement learning (2018, methodology): “Fluid simulators can be categorized according to the discretization methods, which include grid-based methods [Enright et al. 2002], particle-based methods [Becker and Teschner 2007], and hybrid methods [Losasso et al. 2008; Zhu and Bridson 2005].”
• A moving least squares material point method with displacement discontinuity and two-way rigid body coupling (2018, methodology): “This issue is more pronounced in MPM than FLIPbased uids [Zhu and Bridson 2005] due to the wider kernel support of quadratic or cubic B-splines required for solids [Ste en et al. 2008].”