258 citations · 258 across the 5 of their papers we have counts for
5 papers
Learning to Estimate Single-View Volumetric Flow Motions without 3D Supervision
Aleksandra Franz, Barbara Solenthaler, Nils Thuerey
We address the challenging problem of jointly inferring the 3D flow and volumetric densities moving in a fluid from a monocular input video with a deep neural network. Despite the…
Global Transport for Fluid Reconstruction with Learned Self-Supervision
Aleksandra Franz, Barbara Solenthaler, Nils Thuerey
We propose a novel method to reconstruct volumetric flows from sparse views via a global transport formulation. Instead of obtaining the space-time function of the observations, we…
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…
Deep Fluids: A Generative Network for Parameterized Fluid Simulations
Byungsoo Kim, Vinicius C. Azevedo, Nils Thuerey +3
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…
Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow
Steffen Wiewel, Moritz Becher, Nils Thuerey
We propose a method for the data-driven inference of temporal evolutions of physical functions with deep learning. More specifically, we target fluid flows, i.e. Navier-Stokes prob…