Fast and accurate airflow simulations in the built environment are critical to provide
acceptable thermal comfort and air quality to the occupants. Computational Fluid Dynamics …

High-resolution (HR) information of fluid flows, although preferable, is usually less
accessible due to limited computational or experimental resources. In many cases, fluid data …

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 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 …

High-fidelity simulation of complex physical systems is exorbitantly expensive and
inaccessible across spatiotemporal scales. Recently, there has been an increasing interest …

The present paper proposes a super-resolution (SR) model based on a convolutional neural
network and applies it to the near-surface temperature in urban areas. The SR model …

Fluid simulations are often performed using the incompressible Navier-Stokes equations
(INSE), leading to sparse linear systems which are difficult to solve efficiently in parallel …

Predicting the fine and intricate details of a turbulent flow field in both space and time from a
coarse input remains a major challenge despite the availability of modern machine learning …

We propose a simple and efficient method for guiding an Eulerian smoke simulation to
match the behavior of a specified velocity field, such as a low-resolution animation of the …

We propose a pure Lagrangian vortex particle dynamics framework to simulate the various
phenomena of turbulent smoke and two-way coupling between fluid and solid. In the …