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@INPROCEEDINGS{Sarma:1041827,
      author       = {Sarma, Rakesh and Hübenthal, Fabian and Orland, Fabian and
                      Terboven, Christian and Lintermann, Andreas},
      title        = {{P}redicting {T}urbulent {B}oundary {L}ayer {F}lows {U}sing
                      {T}ransformers {C}oupled to the {M}ulti-{P}hysics
                      {S}imulation {T}ool m-{AIA}},
      volume       = {69},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2025-02459},
      series       = {Schriften des Forschungszentrums Jülich IAS Series},
      pages        = {76 - 79},
      year         = {2025},
      comment      = {Proceedings of the 35th Parallel CFD International
                      Conference 2024},
      booktitle     = {Proceedings of the 35th Parallel CFD
                       International Conference 2024},
      abstract     = {Time-marching of turbulent flow fields is computationally
                      expensive with traditional numerical solvers. In this
                      regard, transformer neural network, which has been largely
                      successful in many other technical and scientific domains,
                      can potentially predict complex flow fields faster compared
                      to physics-based solvers. In this study, a transformer model
                      is trained for a turbulent boundary layer problem, which is
                      then coupled to the multi-physics solver m-AIA to make
                      predictions of velocity fields. The method can potentially
                      contribute to significant reduction in computational effort
                      while maintaining high accuracy.},
      month         = {Sep},
      date          = {2024-09-02},
      organization  = {35th Parallel CFD International
                       Conference 2024, Bonn (Germany), 2 Sep
                       2024 - 4 Sep 2024},
      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 / PUB:(DE-HGF)7},
      doi          = {10.34734/FZJ-2025-02459},
      url          = {https://juser.fz-juelich.de/record/1041827},
}