45 citations · 87 across the 2 of their papers we have counts for
8 papers
Smoothed Particle Hydrodynamics Techniques for the Physics Based Simulation of Fluids and Solids
Dan Koschier, Jan Bender, Barbara Solenthaler +1
Graphics research on Smoothed Particle Hydrodynamics (SPH) has produced fantastic visual results that are unique across the board of research communities concerned with SPH simulat…
Lagrangian Neural Style Transfer for Fluids
Byungsoo Kim, Vinicius C. Azevedo, Markus Gross +1
Artistically controlling the shape, motion and appearance of fluid simulations pose major challenges in visual effects production. In this paper, we present a neural style transfer…
Latent Space Subdivision: Stable and Controllable Time Predictions for Fluid Flow
Steffen Wiewel, Byungsoo Kim, Vinicius C. Azevedo +2
We propose an end-to-end trained neural networkarchitecture to robustly predict the complex dynamics of fluid flows with high temporal stability. We focus on single-phase smoke sim…
Frequency-Aware Reconstruction of Fluid Simulations with Generative Networks
Simon Biland, Vinicius C. Azevedo, Byungsoo Kim +1
Convolutional neural networks were recently employed to fully reconstruct fluid simulation data from a set of reduced parameters. However, since (de-)convolutions traditionally tra…
Neural Smoke Stylization with Color Transfer
Fabienne Christen, Byungsoo Kim, Vinicius C. Azevedo +1
Artistically controlling fluid simulations requires a large amount of manual work by an artist. The recently presented transportbased neural style transfer approach simplifies work…
Transport-Based Neural Style Transfer for Smoke Simulations
Byungsoo Kim, Vinicius C. Azevedo, Markus Gross +1
Artistically controlling fluids has always been a challenging task. Optimization techniques rely on approximating simulation states towards target velocity or density field configu…