10 citations · 22 across the 6 of their papers we have counts for
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physics.flu-dyn2022
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame Kernel Direct Numerical Simulation Data
Mathis Bode, Michael Gauding, Dominik Goeb +2
Models for finite-rate-chemistry in underresolved flows still pose one of the main challenges for predictive simulations of complex configurations. The problem gets even more chall…
physics.flu-dyn2022★ 9 cited
Towards prediction of turbulent flows at high Reynolds numbers using high performance computing data and deep learning
Mathis Bode, Michael Gauding, Jens Henrik Göbbert +3
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 suitab…