A parallel multigrid Poisson solver for fluids simulation on large grids

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Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow (2018, methodology): “ng 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 context of fluid simu”
Data-driven synthesis of smoke flows with CNN-based feature descriptors (2017, unknown): “r resolutions, computing a pressure ˙eld to make the ˚ow divergence-free can become a bottleneck. Di˛erent methods to speed up the necessary calculations have been proposed, e.g., multi-grid solvers [McAdams et al. 2010], coarse projections [Lentine et al. 2010], or lower-dimensional approximations ”
Computational Physics on Graphics Processing Units (2012, background): “Equation 2 can also be translated into a linear algebra problem using finite differences, and solved iteratively on multigrids [26,27] in theoretically O(M) operations.”
Data‐driven projection method in fluid simulation (2016, methodology): “McAdams et al. [10] proposed multi-grid as a preprocessing step of conjugate gradient method.”
Accelerating Eulerian Fluid Simulation With Convolutional Networks (2016, methodology): “(McAdams et al., 2010) proposed a multi-grid approach as a preprocessing step of the PCG method.”