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@INPROCEEDINGS{Kruter:874547,
      author       = {Kräuter, Robert and Krasnov, Dmitry and Pandey, Ambrish
                      and Schneide, Christiane and Padberg-Gehle, Kathrin and
                      Giannakis, Dimitrios and Sreenivasan, Katepalli R. and
                      Schumacher, Jörg},
      title        = {{M}achine {L}earning {A}pplications in {C}onvective
                      {T}urbulence},
      volume       = {50},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2020-01502},
      series       = {Publication Series of the John von Neumann Institute for
                      Computing (NIC) NIC Series},
      pages        = {357 - 366},
      year         = {2020},
      comment      = {NIC Symposium 2020},
      booktitle     = {NIC Symposium 2020},
      abstract     = {Turbulent convection flows are ubiquitous in natural
                      systems such as in the atmosphere or in stellar interiors as
                      well as in technological applications such as cooling or
                      energy storage devices. Their physical complexity and vast
                      number of degrees of freedom prevents often an access by
                      direct numerical simulations that resolve all flow scales
                      from the smallest to the largest plumes and vortices in the
                      system and requires a simplified modelling of the flow
                      itself and the resulting turbulent transport behaviour. The
                      following article summarises some examples that aim at a
                      reduction of the flow complexity and thus of the number of
                      degrees of freedom of convective turbulence by machine
                      learning approaches. We therefore apply unsupervised and
                      supervised machine learning methods to direct numerical
                      simulation data of a Rayleigh-Bénard convection flow which
                      serves as a paradigm of the examples mentioned at the
                      beginning.},
      month         = {Feb},
      date          = {2020-02-27},
      organization  = {NIC Symposium 2020, Jülich (Germany),
                       27 Feb 2020 - 28 Feb 2020},
      cin          = {NIC},
      cid          = {I:(DE-Juel1)NIC-20090406},
      pnm          = {899 - ohne Topic (POF3-899)},
      pid          = {G:(DE-HGF)POF3-899},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      url          = {https://juser.fz-juelich.de/record/874547},
}