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@ARTICLE{Higashida:1026510,
      author       = {Higashida, Aito and Ando, Kazuto and Rüttgers, Mario and
                      Lintermann, Andreas and Tsubokura, Makoto},
      title        = {{R}obustness evaluation of large-scale machine
                      learning-based reduced order models for reproducing flow
                      fields},
      journal      = {Future generation computer systems},
      volume       = {159},
      issn         = {0167-739X},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2024-03447},
      pages        = {243 - 254},
      year         = {2024},
      abstract     = {The robustness of an artificial neural network that
                      performs model order reduction for flow field data is
                      studied. The network is trained with a large-scale
                      distributed learning approach using up to 6,259 nodes of the
                      supercomputer Fugaku. Flow around two square cylinders with
                      a varying distance between their centers is investigated.
                      The network is trained and tested with data from numerical
                      simulations. First, the capability to reproduce flow fields
                      with 2, 12, and 24 modes is investigated by comparing the
                      reconstructed flow data to simulated data. It is shown, that
                      reconstructions based on 2 modes cannot capture both, low-
                      and high-frequency flow structures correctly, whereas
                      predictions based on 12 and 24 modes yield improved flow
                      fields, especially in the case of high-frequency waves in
                      the vicinity of the square cylinders. Reconstructions with
                      24 modes provide smooth velocity fields that reproduce all
                      relevant low- and high-frequency waves for all variations of
                      the distance between the two square cylinders. Second, the
                      performance of the machine learning-based reconstructions
                      are compared to proper orthogonal decomposition, which is a
                      commonly used reduced order model technique. The comparison
                      only includes flow fields based on 24 modes. For all
                      geometric variations, the mean squared errors of the
                      reconstructions by the conventional method are higher than
                      those of the machine learning model. This underlines the
                      advantage of artificial neural networks over linear methods
                      like proper orthogonal decomposition for tasks like
                      reconstructing flow fields that are characterized by
                      non-linear governing equations.},
      cin          = {JSC},
      ddc          = {004},
      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) /
                      JLESC - Joint Laboratory for Extreme Scale Computing
                      (JLESC-20150708)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)951733 /
                      G:(DE-Juel1)JLESC-20150708},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001247468300001},
      doi          = {10.1016/j.future.2024.05.005},
      url          = {https://juser.fz-juelich.de/record/1026510},
}