Implicit Incompressible SPH

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Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow (2018, background): “ion, as the underlying iterative solver is often the most expensive component in an algorithm. E.g., techniques for dimensionality reduction [Ando et al. 2015; Lentine et al. 2010], and fast solvers [Ihmsen et al. 2014; McAdams et al. 2010] have been proposed to diminish its runtime impact. In the c”
Thin-film smoothed particle hydrodynamics fluid (2021, positive): “The simplicity and high parallelizability make SPH useful for interactive fluid simulation [Müller et al. 2003], and can readily operate in conjunction with other fluid simulation algorithms [Band et al. 2018; Cornelis et al. 2014; Ihmsen et al. 2013].”
Divergence-free smoothed particle hydrodynamics (2015, methodology): “In order to fulfill the conditions globally the solvers process the neighborhoods in a parallel Jacobi fashion which is a common choice in SPH solvers [Solenthaler and Pajarola 2009; Macklin and Müller 2013; Ihmsen et al. 2014a].”
Data-driven fluid simulations using regression forests (2015, methodology): “The performance has been further improved by discretizing and iteratively solving the pressure Poisson equation [Ihmsen et al. 2013].”
The affine particle-in-cell method (2015, methodology): “[Cornelis et al. 2014] couple high-resolution FLIP with a low-resolution implicit Smoothed Particle Hydrodynamics (SPH) from [Ihmsen et al. 2013].”