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Deep Fluids: A Generative Network for Parameterized Fluid Simulations
(The Eurographics Association and John Wiley & Sons Ltd., 2019)
This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters. A convolutional neural network is trained on a collection of discrete, parameterizable fluid simulation velocity ...
Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow
(The Eurographics Association and John Wiley & Sons Ltd., 2019)
We propose a method for the data-driven inference of temporal evolutions of physical functions with deep learning. More specifically, we target fluid flow problems, and we propose a novel LSTM-based approach to predict the ...
Latent Space Subdivision: Stable and Controllable Time Predictions for Fluid Flow
(The Eurographics Association and John Wiley & Sons Ltd., 2020)
We propose an end-to-end trained neural network architecture to robustly predict the complex dynamics of fluid flows with high temporal stability. We focus on single-phase smoke simulations in 2D and 3D based on the ...