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001007692 041__ $$aEnglish
001007692 1001_ $$0P:(DE-Juel1)188513$$aSarma, Rakesh$$b0$$eCorresponding author
001007692 1112_ $$a33rd International Conference on Parallel Computational Fluid Dynamics$$cAlba$$d2022-05-25 - 2022-05-27$$gParCFD2022$$wItaly
001007692 245__ $$aParallel and Scalable Deep Learning to Reconstruct Actuated Turbulent Boundary Layer Flows. Part I: Investigation of Autoencoder-Based Trainings
001007692 260__ $$c2022
001007692 300__ $$a4 pages
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001007692 520__ $$aWith 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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001007692 536__ $$0G:(EU-Grant)951733$$aRAISE - Research on AI- and Simulation-Based Engineering at Exascale (951733)$$c951733$$fH2020-INFRAEDI-2019-1$$x1
001007692 7001_ $$0P:(DE-HGF)0$$aAlbers, Marian$$b1
001007692 7001_ $$0P:(DE-Juel1)188268$$aInanc, Eray$$b2
001007692 7001_ $$0P:(DE-Juel1)180916$$aAach, Marcel$$b3$$ufzj
001007692 7001_ $$0P:(DE-HGF)0$$aSchröder, Wolfgang$$b4
001007692 7001_ $$0P:(DE-Juel1)165948$$aLintermann, Andreas$$b5
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