Smoothed particle hydrodynamics (SPH), is a meshfree, Lagrangian, particle method, which
is advantageous over conventional grid-based numerical methods in the aspect of interface …

We present an approach to Lagrangian fluid simulation with a new type of convolutional
network. Our networks process sets of moving particles, which describe fluids in space and …

In fluid simulation, enforcing incompressibility is crucial for realism; it is also computationally
expensive. Recent work has improved efficiency, but still requires time-steps that are …

Traditional fluid simulations require large computational resources even for an average
sized scene with the main bottleneck being a very small time step size, required to …

Physics-based fluid simulation has played an increasingly important role in the computer
graphics community. Recent methods in this area have greatly improved the generation of …

We propose a novel formulation of the projection method for Smoothed Particle
Hydrodynamics (SPH). We combine a symmetric SPH pressure force and an SPH …

We propose a momentum-conserving two-way coupling method of SPH fluids and arbitrary
rigid objects based on hydrodynamic forces. Our approach samples the surface of rigid …

Computational offloading services at the edge of the Internet for mobile devices are
becoming a reality. Using a wide range of mobile applications, we explore how such …

We present a novel method for guaranteeing linear momentum in learned physics
simulations. Unlike existing methods, we enforce conservation of momentum with a hard …

We present a data-driven model for fluid simulation under Lagrangian representation. Our
model, Fluid Graph Networks (FGN), uses graphs to represent the fluid field. In FGN, fluid …