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@INPROCEEDINGS{Sarma:1007692,
      author       = {Sarma, Rakesh and Albers, Marian and Inanc, Eray and Aach,
                      Marcel and Schröder, Wolfgang and Lintermann, Andreas},
      title        = {{P}arallel and {S}calable {D}eep {L}earning to
                      {R}econstruct {A}ctuated {T}urbulent {B}oundary {L}ayer
                      {F}lows. {P}art {I}: {I}nvestigation of
                      {A}utoencoder-{B}ased {T}rainings},
      reportid     = {FZJ-2023-02166},
      pages        = {4 pages},
      year         = {2022},
      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.},
      month         = {May},
      date          = {2022-05-25},
      organization  = {33rd International Conference on
                       Parallel Computational Fluid Dynamics,
                       Alba (Italy), 25 May 2022 - 27 May
                       2022},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / RAISE - Research on
                      AI- and Simulation-Based Engineering at Exascale (951733)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)951733},
      typ          = {PUB:(DE-HGF)8},
      url          = {https://juser.fz-juelich.de/record/1007692},
}