Deep learning at scale for subgrid modeling in turbulent flows

@inproceedings{Bode2019DeepLA,
  title={Deep learning at scale for subgrid modeling in turbulent flows},
  author={Mathis Bode and Michael Gauding and Konstantin Kleinheinz and Heinz Pitsch},
  booktitle={ISC Workshops},
  year={2019}
}
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… 

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