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| Hauptseite > Publikationsdatenbank > Parallel and Scalable Deep Learning to Reconstruct Actuated Turbulent Boundary Layer Flows. Part I: Investigation of Autoencoder-Based Trainings |
| Hauptseite > Publikationsdatenbank > Parallel and Scalable Deep Learning to Reconstruct Actuated Turbulent Boundary Layer Flows. Part I: Investigation of Autoencoder-Based Trainings |
| Contribution to a conference proceedings | FZJ-2023-02166 |
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2022
Please use a persistent id in citations: http://hdl.handle.net/2128/34555
Abstract: 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.
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