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@INPROCEEDINGS{Bode:917410,
      author       = {Bode, Mathis},
      title        = {{A}pplying {P}hysics-{I}nformed {E}nhanced
                      {S}uper-{R}esolution {G}enerative {A}dversarial {N}etworks
                      to {L}arge-{E}ddy {S}imulations of {ECN} {S}pray {C}},
      reportid     = {FZJ-2023-00624},
      series       = {SAE Technical Paper},
      pages        = {1-9},
      year         = {2022},
      month         = {Apr},
      date          = {2022-04-05},
      organization  = {SAE WCX World Congress Experience,
                       Detroit (USA), 5 Apr 2022 - 7 Apr 2022},
      cin          = {JSC},
      cid          = {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)8 / PUB:(DE-HGF)7},
      url          = {https://juser.fz-juelich.de/record/917410},
}