Our ERC Starting Grant “realFlow” is finished by now (it was concluded in April 2020), but nonetheless good to see it on the CORDIS website:
https://cordis.europa.eu/article/id/429170-teaching-neural-networks-to-go-with-the-flow?WT.mc_id=exp
The nice image there is from our temporally-coherent fluid GAN (tempoGAN), published in 2018 at SIGGRAPH. Interestingly, since then few works were able to handle 4D data sets (3D volumes over time) while taking into account how the learned functions should change over time.
The cutout above is from our largest example, with a resolution of 1024 × 720 × 720 cells over 200 time steps. That means the CNN generated a total number of 6,794,772,480,000 cells (i.e., more than 6 trillion cells) for this sequence.
