Predictive-corrective incompressible SPH

By Barbara Solenthaler, Renato Pajarola. ACM Transactions on Graphics, 2009

471 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics

View Paper: https://www.semanticscholar.org/paper/16f66395e00c09ca0a34e78bcf465f8256f02ed4

How researchers see this paper

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

In Biomedical Engineering

1. Nils Thürey
2. Miles Macklin
3. Matthias Müller
4. Dan Koschier
5. Jan Bender
6. Lukas Prantl
7. Vladlen Koltun
8. Greg Turk

In Computational Mechanics

1. Nils Thürey
2. Lukas Prantl
3. Miles Macklin
4. Matthias Müller
5. Jan Bender
6. Vladlen Koltun
7. Benjamin Ummenhofer
8. Dan Koschier

Citations in the corpus

• OpenWorm: an open-science approach to modeling Caenorhabditis elegans (2014, methodology): “Sibernetic is based on the predictive-corrective incompressible smoothed particle hydrodynamics (PCISPH) algorithm (Solenthaler and Pajarola, 2009) with modifications to incorporate boundary-handling as proposed by Ihmsen et al. (2010) and a surface tension model based on Becker and Teschner (2007).”
• Thin-film smoothed particle hydrodynamics fluid (2021, unknown): “…(SPH) [Brookshaw 1985; Gingold and Monaghan 1977; Koschier et al. 2019; Liu and Liu 2010; Monaghan 1992; Monaghan and Lattanzio 1985; Morris et al. 1997; Solenthaler and Pajarola 2009], where discretization formulation based on radial smoothing kernels is used to approximate differential operators.”
• Data-driven fluid simulations using regression forests (2015, methodology): “A predictive-corrective scheme has been introduced where pressure values are iteratively updated to satisfy the zero compression constraint [Solenthaler and Pajarola 2009].”
• Lagrangian Fluid Simulation with Continuous Convolutions (2020, methodology): “Following the earlier development of physics-based fluid simulation for special effects by Foster & Metaxas (1996), the introduction of SPH (M¨uller et al., 2003) has led to a large class of powerful algorithms (Solenthaler & Pajarola, 2009; Bender & Koschier, 2015).”
• Divergence-free smoothed particle hydrodynamics (2015, methodology): “Hence, implicit pressure solvers, e.g. [Solenthaler and Pajarola 2009; Ihmsen et al. 2014a], were investigated to simulate incompressible fluids.”
• Position based fluids (2013, methodology): “Predictive-corrective incompressible SPH (PCISPH) [Solenthaler and Pajarola 2009] relaxes this timestep restriction by using an iterative Jacobi-style method that accu- ACM Transactions on Graphics, Vol. 32, No. 4, Article 104, Publication Date: July 2013 mulates pressure changes and applies forces…”
• Versatile rigid-fluid coupling for incompressible SPH (2012, methodology): “A similar assumption has been also used in the derivation of PCISPH [Solenthaler and Pajarola 2009].”
• Blending liquids (2014, methodology): “In addition to such Eulerian approaches, the state-of-the-art is steadily advanced by purely particle-based [M¨uller et al. 2003; Solenthaler and Pajarola 2009] and hybrid approaches [Zhu and Bridson 2005].”