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@INPROCEEDINGS{Rttgers:885828,
      author       = {Rüttgers, Mario and Koh, Seong-Ryong and Jitsev, Jenia and
                      Schröder, Wolfgang and Lintermann, Andreas},
      title        = {{P}rediction of {A}coustic {F}ields {U}sing a
                      {L}attice-{B}oltzmann {M}ethod and {D}eep {L}earning},
      volume       = {12321},
      address      = {Cham},
      publisher    = {Springer},
      reportid     = {FZJ-2020-04119},
      isbn         = {978-3-030-59850-1},
      series       = {Lecture Notes in Computer Science},
      pages        = {81-101},
      year         = {2020},
      comment      = {High Performance Computing. ISC High Performance 2020.
                      Lecture Notes in Computer Science},
      booktitle     = {High Performance Computing. ISC High
                       Performance 2020. Lecture Notes in
                       Computer Science},
      abstract     = {Using traditional computational fluid dynamics and
                      aeroacoustics methods, the accurate simulation of
                      aeroacoustic sources requires high compute resources to
                      resolve all necessary physical phenomena. In contrast, once
                      trained, artificial neural networks such as deep
                      encoder-decoder convolutional networks allow to predict
                      aeroacoustics at lower cost and, depending on the quality of
                      the employed network, also at high accuracy. The
                      architecture for such a neural network is developed to
                      predict the sound pressure level in a 2D square domain. It
                      is trained by numerical results from up to 20,000 GPU-based
                      lattice-Boltzmann simulations that include randomly
                      distributed rectangular and circular objects, and monopole
                      sources. Types of boundary conditions, the monopole
                      locations, and cell distances for objects and monopoles
                      serve as input to the network. Parameters are studied to
                      tune the predictions and to increase their accuracy. The
                      complexity of the setup is successively increased along
                      three cases and the impact of the number of feature maps,
                      the type of loss function, and the number of training data
                      on the prediction accuracy is investigated. An optimal
                      choice of the parameters leads to network-predicted results
                      that are in good agreement with the simulated findings. This
                      is corroborated by negligible differences of the sound
                      pressure level between the simulated and the
                      network-predicted results along characteristic lines and by
                      small mean errors.},
      month         = {Jun},
      date          = {2020-06-22},
      organization  = {ISC High Performance 2020, Frankfurt
                       (Germany), 22 Jun 2020 - 25 Jun 2020},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / 512 - Data-Intensive Science and Federated
                      Computing (POF3-512) / PhD no Grant - Doktorand ohne
                      besondere Förderung (PHD-NO-GRANT-20170405)},
      pid          = {G:(DE-HGF)POF3-511 / G:(DE-HGF)POF3-512 /
                      G:(DE-Juel1)PHD-NO-GRANT-20170405},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1007/978-3-030-59851-8_6},
      url          = {https://juser.fz-juelich.de/record/885828},
}