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 …

The long runtime of high-fidelity partial differential equation (PDE) solvers makes them
unsuitable for time-critical applications. We propose to accelerate PDE solvers using …

We introduce Neural Flow Maps, a novel simulation method bridging the emerging
paradigm of implicit neural representations with fluid simulation based on the theory of flow …

This study presents the formulation and validation of a three-dimensional Flow Lattice Model
(FLM) with application to the Hygro-Thermo-Chemical (HTC) model for analysis of moisture …

We propose a novel Particle Flow Map (PFM) method to enable accurate long-range
advection for incompressible fluid simulation. The foundation of our method is the …

This paper introduces a novel Lagrangian fluid solver based on covector flow maps. We aim
to address the challenges of establishing a robust flow-map solver for incompressible fluids …

We derive the spin Euler equation for ideal flows by applying the spherical Clebsch
mapping. This equation is based on the spin vector, a unit vector field encoding vortex lines …

We present a velocity-based Monte Carlo fluid solver that overcomes the limitations of its
existing vorticity-based counterpart. Because the velocity-based formulation is more …

Level-set methods play a fundamental role in simulating free-surface flow. By representing
the fluid volume with a signed distance function, level-set methods can simultaneously …

The idea of using a neural network to represent continuous vector fields (ie, neural fields)
has become popular for solving PDEs arising from physics simulations. Here, the classical …