A Novel CNN-Based Poisson Solver for Fluid Simulation

Solving a large-scale Poisson system is computationally expensive for most of the Eulerian fluid simulation applications. We propose a novel machine learning-based approach to accelerate this process. At the heart of our approach is a deep convolutional neural network (CNN), with the capability of predicting the solution (pressure) of a Poisson system given the discretization structure and the intermediate velocities as input. Our system consists of four main components, namely, a deep neural network to solve the large linear equations, a geometric structure to describe the spatial hierarchies of the input vector, a Principal Component Analysis (PCA) process to reduce the dimension of input in training, and a novel loss function to control the incompressibility constraint. We have demonstrated the efficacy of our approach by simulating a variety of high-resolution smoke and liquid phenomena. In particular, we have shown that our approach accelerates the projection step in a conventional Eulerian fluid simulator by two orders of magnitude. In addition, we have also demonstrated the generality of our approach by producing a diversity of animations deviating from the original datasets.

Publication: Xiao X, Zhou Y, Wang H, et al. A novel cnn-based poisson solver for fluid simulation[J]. IEEE transactions on visualization and computer graphics, 2018.

Preprint PDF: A Novel CNN-based Poisson Solver for Fluid Simulation


title={A novel cnn-based poisson solver for fluid simulation},
author={Xiao, Xiangyun and Zhou, Yanqing and Wang, Hui and Yang, Xubo},
journal={IEEE transactions on visualization and computer graphics},