83 citations · 140 across the 5 of their papers we have counts for
3 papers · 1 filter
Neural Importance Sampling
Thomas Müller, Brian McWilliams, Fabrice Rousselle +2
We propose to use deep neural networks for generating samples in Monte Carlo integration. Our work is based on non-linear independent components estimation (NICE), which we extend…
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…
Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks
Simon Kallweit, Thomas Müller, Brian McWilliams +2
We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks. The intricacies of Lorenz-Mie s…