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Found 6 papers, 3 papers with code
Lagrangian Neural Style Transfer for Fluids
Artistically controlling the shape, motion and appearance of fluid simulations pose major challenges in visual effects production.
Latent Space Subdivision: Stable and Controllable Time Predictions for Fluid Flow
By selectively overwriting parts of the predicted latent space points, our proposed method is capable to robustly predict long-term sequences of complex physics problems.
Neural Smoke Stylization with Color Transfer
Artistically controlling fluid simulations requires a large amount of manual work by an artist.
Graphics
Frequency-Aware Reconstruction of Fluid Simulations with Generative Networks
Convolutional neural networks were recently employed to fully reconstruct fluid simulation data from a set of reduced parameters.
Transport-Based Neural Style Transfer for Smoke Simulations
Optimization techniques rely on approximating simulation states towards target velocity or density field configurations, which are often handcrafted by artists to indirectly control smoke dynamics.
Deep Fluids: A Generative Network for Parameterized Fluid Simulations
This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters.
