Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows

no code implementations • 26 Nov 2019 • Mathis Bode, Michael Gauding, Zeyu Lian, Dominik Denker, Marco Davidovic, Konstantin Kleinheinz, Jenia Jitsev, Heinz Pitsch

Reasons for this are the large amount of degrees of freedom in realistic flows, the high requirements with respect to accuracy and error robustness, as well as open questions, such as the generalization capability of trained neural networks in such high-dimensional, physics-constrained scenarios.

Generative Adversarial Network Super-Resolution

Deep learning at scale for subgrid modeling in turbulent flows

no code implementations • 1 Oct 2019 • Mathis Bode, Michael Gauding, Konstantin Kleinheinz, Heinz Pitsch

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.

regression Super-Resolution