%0 Conference Paper
%A Sarma, Rakesh
%A Albers, Marian
%A Inanc, Eray
%A Aach, Marcel
%A Schröder, Wolfgang
%A Lintermann, Andreas
%T Parallel and Scalable Deep Learning to Reconstruct Actuated Turbulent Boundary Layer Flows. Part I: Investigation of Autoencoder-Based Trainings
%M FZJ-2023-02166
%P 4 pages
%D 2022
%X 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.
%B 33rd International Conference on Parallel Computational Fluid Dynamics
%C 25 May 2022 - 27 May 2022, Alba (Italy)
Y2 25 May 2022 - 27 May 2022
M2 Alba, Italy
%F PUB:(DE-HGF)8
%9 Contribution to a conference proceedings
%U https://juser.fz-juelich.de/record/1007692