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 …

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 …

Purpose The purpose of this study is to advance the computational modeling of liquid
splashing dynamics, while balancing simulation accuracy and computational efficiency, a …

In this work, we introduce FluidsFormer: a transformer-based approach for fluid interpolation
within a continuous-time framework. By combining the capabilities of PITT and a residual …

This chapter surveys deep learning models for fluid simulation and rendering. Regarding the
former, we can group the works into small and large-scale fluid simulation, the interaction of …

This book is an introduction to the use of machine learning and data-driven approaches in
fluid simulation and animation, as an alternative to traditional modeling techniques based on …

In this paper, we propose CIMS: a novel correction-interpolation method for smoke
simulation. The basis of our method is to first generate a low frame rate smoke simulation …

As shown in the previous chapters, fluid simulation deals with complex models and
algorithms to approximate the myriad of behaviors fluids may have. Those simulations …

In recent years, there has been a growing interest in editing within the latent space, posing a
significant challenge in delivering controllable data-driven capabilities. Among many other …