. Our convolutional neural network learns to generate highly detailed, and temporally coherent features based on a low-resolution field containing a single time-step of density and velocity data. We introduce a novel discriminator that ensures the synthesized details change smoothly over time. We propose a temporally coherent generative model addressing the superresolution problem for fluid flows. Our work represents a first approach to synthesize four-dimensional physics fields with neural networks. Based on a conditional generative adversarial network that is designed for the inference of three-dimensional volumetric data, our model generates consistent and detailed results by using a novel temporal discriminator, in addition to the commonly used spatial one. Our experiments show that the generator is able to infer more realistic high-resolution details by using additional physical quantities, such as low-resolution velocities or vorticities. Besides improvements in the training process and in the generated outputs, these inputs offer means for artistic control as well.
A temporally coherent Generative Adversarial Network (GAN) was designed for smoke simulation super-resolution REF , removing the reliance on Lagrangian tracking of features of the previous approach.
tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow
ACM Trans. Graph. 37, 4, Article 95 (July 2018)
Mathematics Computer Science