TY  - CONF
AU  - Sarma, Rakesh
AU  - Albers, Marian
AU  - Inanc, Eray
AU  - Aach, Marcel
AU  - Schröder, Wolfgang
AU  - Lintermann, Andreas
TI  - Parallel and Scalable Deep Learning to Reconstruct Actuated Turbulent Boundary Layer Flows. Part I: Investigation of Autoencoder-Based Trainings
M1  - FZJ-2023-02166
SP  - 4 pages
PY  - 2022
AB  - With the availability of large datasets and increasing high-performance computing resources, machine learning tools offer many opportunities to improve and/or augment numerical methods used in the field of computational fluid dynamics. A low-dimensional representation of a turbulent boundary layer flow field is generated by a plain and a physics-contrained autoencoder. The training makes use of a distributed learning environment. The average test error of the plain autoencoder is ~4.4 times smaller than the error of the physics-constrained autoencoder although the latter integrates physical laws in the training process. Furthermore, after 1,000 epochs, the training  loss of the  physics-constrained autoencoder is ~9.1 times higher than the plain autoencoder after 300 epochs. The neural network corresponding to the plain autoencoder is able to provide accurate reconstructions of a turbulent boundary layer flow.
T2  - 33rd International Conference on Parallel Computational Fluid Dynamics
CY  - 25 May 2022 - 27 May 2022, Alba (Italy)
Y2  - 25 May 2022 - 27 May 2022
M2  - Alba, Italy
LB  - PUB:(DE-HGF)8
UR  - https://juser.fz-juelich.de/record/1007692
ER  -