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- W2894820511 abstract "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." @default.
- W2894820511 created "2018-10-12" @default.
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- W2894820511 date "2020-03-01" @default.
- W2894820511 modified "2023-10-10" @default.
- W2894820511 title "A Novel CNN-Based Poisson Solver for Fluid Simulation" @default.
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- W2894820511 doi "https://doi.org/10.1109/tvcg.2018.2873375" @default.
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