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@INPROCEEDINGS{Bode:859970,
      author       = {Bode, Mathis and Gauding, Michael and Göbbert, Jens Henrik
                      and Liao, Baohao and Jitsev, Jenia and Pitsch, Heinz},
      title        = {{T}owards {P}rediction of {T}urbulent {F}lows at {H}igh
                      {R}eynolds {N}umbers {U}sing {H}igh {P}erformance
                      {C}omputing {D}ata and {D}eep {L}earning},
      volume       = {11203},
      address      = {Cham},
      publisher    = {Springer International Publishing},
      reportid     = {FZJ-2019-00776},
      isbn         = {978-3-030-02464-2 (print)},
      series       = {Lecture Notes in Computer Science},
      pages        = {614 - 623},
      year         = {2018},
      comment      = {High Performance Computing},
      booktitle     = {High Performance Computing},
      abstract     = {In this paper, deep learning (DL) methods are evaluated in
                      the context of turbulent flows. Various generative
                      adversarial networks (GANs) are discussed with respect to
                      their suitability for understanding and modeling turbulence.
                      Wasserstein GANs (WGANs) are then chosen to generate
                      small-scale turbulence. Highly resolved direct numerical
                      simulation (DNS) turbulent data is used for training the
                      WGANs and the effect of network parameters, such as learning
                      rate and loss function, is studied. Qualitatively good
                      agreement between DNS input data and generated turbulent
                      structures is shown. A quantitative statistical assessment
                      of the predicted turbulent fields is performed.},
      month         = {Jun},
      date          = {2018-06-24},
      organization  = {International Conference on High
                       Performance Computing, Frankfurt
                       (Germany), 24 Jun 2018 - 28 Jun 2018},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / Using deep learning to predict statistics of
                      turbulent flows at high Reynolds numbers
                      $(jhpc55_20180501)$},
      pid          = {G:(DE-HGF)POF3-511 / $G:(DE-Juel1)jhpc55_20180501$},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      UT           = {WOS:000612998200051},
      doi          = {10.1007/978-3-030-02465-9_44},
      url          = {https://juser.fz-juelich.de/record/859970},
}