Towards prediction of turbulent flows at high Reynolds numbers using high performance computing data and deep learning. (arXiv:2210.16110v1 [physics.flu-dyn])

In this paper, deep learning (DL) methods are evaluated in the context of
turbulent flows. Various generative adversarial networks (GANs) are discussed
with respect to their suitability for understanding and modeling turbulence.
Wasserstein GANs (WGANs) are then chosen to generate small-scale turbulence.
Highly resolved direct numerical simulation (DNS) turbulent data is used for
training the WGANs and the effect of network parameters, such as learning rate
and loss function, is studied. Qualitatively good agreement between DNS input
data and generated …

arxiv computing data deep learning high performance computing numbers performance physics prediction