  Modeling of turbulent flows is still challenging. One way to deal with the
large scale separation due to turbulence is to simulate only the large scales
and model the unresolved contributions as done in large-eddy simulation (LES).
This paper focuses on two deep learning (DL) strategies, regression and
reconstruction, which are data-driven and promising alternatives to classical
modeling concepts. Using three-dimensional (3-D) forced turbulence direct
numerical simulation (DNS) data, subgrid models are evaluated, which predict
the unresolved part of quantities based on the resolved solution. For
regression, it is shown that feedforward artificial neural networks (ANNs) are
able to predict the fully-resolved scalar dissipation rate using filtered input
data. It was found that a combination of a large-scale quantity, such as the
filtered passive scalar itself, and a small-scale quantity, such as the
filtered energy dissipation rate, gives the best agreement with the actual DNS
data. Furthermore, a DL network motivated by enhanced super-resolution
generative adversarial networks (ESRGANs) was used to reconstruct
fully-resolved 3-D velocity fields from filtered velocity fields. The energy
spectrum shows very good agreement. As size of scientific data is often in the
order of terabytes or more, DL needs to be combined with high performance
computing (HPC). Necessary code improvements for HPC-DL are discussed with
respect to the supercomputer JURECA. After optimizing the training code, 396.2
TFLOPS were achieved.
