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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 > print |
| Hauptseite > Publikationsdatenbank > Parallel and Scalable Deep Learning to Reconstruct Actuated Turbulent Boundary Layer Flows. Part I: Investigation of Autoencoder-Based Trainings > print |
| 001 | 1007692 | ||
| 005 | 20240226075321.0 | ||
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| 037 | _ | _ | |a FZJ-2023-02166 |
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| 100 | 1 | _ | |a Sarma, Rakesh |0 P:(DE-Juel1)188513 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a 33rd International Conference on Parallel Computational Fluid Dynamics |g ParCFD2022 |c Alba |d 2022-05-25 - 2022-05-27 |w Italy |
| 245 | _ | _ | |a Parallel and Scalable Deep Learning to Reconstruct Actuated Turbulent Boundary Layer Flows. Part I: Investigation of Autoencoder-Based Trainings |
| 260 | _ | _ | |c 2022 |
| 300 | _ | _ | |a 4 pages |
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| 520 | _ | _ | |a 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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| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1007692/files/2022_ParCFD_Abstract_Sarma.pdf |y OpenAccess |
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