10 citations · 22 across the 6 of their papers we have counts for
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cs.LG2019★ 10 cited
Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows
Mathis Bode, Michael Gauding, Zeyu Lian +5
Turbulence is still one of the main challenges for accurately predicting reactive flows. Therefore, the development of new turbulence closures which can be applied to combustion pr…
physics.comp-ph2019★ 3 cited
Deep learning at scale for subgrid modeling in turbulent flows
Mathis Bode, Michael Gauding, Konstantin Kleinheinz +1
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 co…