no code implementations • 28 Oct 2022 • Mathis Bode, Michael Gauding, Dominik Goeb, Tobias Falkenstein, Heinz Pitsch
The resulting model provides good results for a priori and a posteriori tests on direct numerical simulation data of a fully turbulent premixed flame kernel.
no code implementations • 28 Oct 2022 • Mathis Bode, Michael Gauding, Jens Henrik Göbbert, Baohao Liao, Jenia Jitsev, Heinz Pitsch
In this paper, deep learning (DL) methods are evaluated in the context of turbulent 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.
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.