Syuhei Sato, Yoshinori Dobashi, T. Kim, and Tomoyuki Nishita Generating realistic fluid simulations remains computationally expensive, and animators can expend enormous effort trying to achieve a desired motion. To reduce such costs, several methods have been developed in which high-resolution turbulence is synthesized as a post process. Since global motion can then be obtained using […]

Takahiro Sato, Chris Wojtan, Nils Thuerey, Takeo Igarashi, Ryoichi Ando The Fluid Implicit Particle method (FLIP) reduces numerical dissipation by combining particles with grids. To improve performance, the subsequent narrow band FLIP method (NB-FLIP) uses a FLIP-based fluid simulation only near the liquid surface and a traditional grid-based fluid simulation away from the surface. This […]

Nobuyuki Umetani, Bernd Bickel We present a data-driven technique to instantly predict how fluid flows around various three-dimensional objects. Such simulation is useful for computational fabrication and engineering, but is usually computationally expensive since it requires solving the Navier-Stokes equation for many time steps. To accelerate the process, we propose a machine learning framework which […]

Qiaodong Cui, Pradeep Sen, Theodore Kim The Laplacian Eigenfunction method for fluid simulation, which we refer to as Eigenfluids, introduced an elegant new way to capture intricate fluid flows with near-zero viscosity. However, the approach does not scale well, as the memory cost grows prohibitively with the number of eigenfunctions. The method also lacks generality, […]