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@ARTICLE{Bode:892464,
      author       = {Bode, Mathis and Gauding, Michael and Lian, Zeyu and
                      Denker, Dominik and Davidovic, Marco and Kleinheinz,
                      Konstantin and Jitsev, Jenia and Pitsch, Heinz},
      title        = {{U}sing physics-informed enhanced super-resolution
                      generative adversarial networks for subfilter modeling in
                      turbulent reactive flows},
      journal      = {Proceedings of the Combustion Institute},
      volume       = {38},
      number       = {2},
      issn         = {1540-7489},
      address      = {New York, NY [u.a.]},
      publisher    = {Elsevier ScienceDirect},
      reportid     = {FZJ-2021-02100},
      pages        = {2617 - 2625},
      year         = {2021},
      cin          = {JARA-HPC / JSC},
      ddc          = {660},
      cid          = {$I:(DE-82)080012_20140620$ / I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / Using deep learning to
                      predict statistics of turbulent flows at high Reynolds
                      numbers $(jhpc55_20190501)$},
      pid          = {G:(DE-HGF)POF4-5112 / $G:(DE-Juel1)jhpc55_20190501$},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000651837700001},
      doi          = {10.1016/j.proci.2020.06.022},
      url          = {https://juser.fz-juelich.de/record/892464},
}