TY  - CONF
AU  - Bode, Mathis
AU  - Gauding, Michael
AU  - Göbbert, Jens Henrik
AU  - Liao, Baohao
AU  - Jitsev, Jenia
AU  - Pitsch, Heinz
TI  - Towards Prediction of Turbulent Flows at High Reynolds Numbers Using High Performance Computing Data and Deep Learning
VL  - 11203
CY  - Cham
PB  - Springer International Publishing
M1  - FZJ-2019-00776
SN  - 978-3-030-02464-2 (print)
T2  - Lecture Notes in Computer Science
SP  - 614 - 623
PY  - 2018
AB  - 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 suitability for understanding and modeling turbulence. Wasserstein GANs (WGANs) are then chosen to generate small-scale turbulence. Highly resolved direct numerical simulation (DNS) turbulent data is used for training the WGANs and the effect of network parameters, such as learning rate and loss function, is studied. Qualitatively good agreement between DNS input data and generated turbulent structures is shown. A quantitative statistical assessment of the predicted turbulent fields is performed.
T2  - International Conference on High Performance Computing
CY  - 24 Jun 2018 - 28 Jun 2018, Frankfurt (Germany)
Y2  - 24 Jun 2018 - 28 Jun 2018
M2  - Frankfurt, Germany
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
UR  - <Go to ISI:>//WOS:000612998200051
DO  - DOI:10.1007/978-3-030-02465-9_44
UR  - https://juser.fz-juelich.de/record/859970
ER  -