  We propose a temporally coherent generative model addressing the
super-resolution 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. We additionally employ a
physics-aware data augmentation step, which is crucial to avoid overfitting and
to reduce memory requirements. In this way, our network learns to generate
advected quantities with highly detailed, realistic, and temporally coherent
features. Our method works instantaneously, using only a single time-step of
low-resolution fluid data. We demonstrate the abilities of our method using a
variety of complex inputs and applications in two and three dimensions.
