% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Calmet:1041828,
      author       = {Calmet, Hadrien and Calafell, Joan and Sarma, Rakesh and
                      Rüttgers, Mario and Lintermann, Andreas and Houzeaux,
                      Guillaume},
      title        = {{C}reating a {V}irtual {P}opulation of the {H}uman {N}asal
                      {C}avity for {V}elocity-{B}ased {P}redictions of
                      {R}espiratory {F}low {F}eatures {U}sing {G}raph
                      {C}onvolutional {N}eural {N}etworks},
      volume       = {69},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2025-02460},
      series       = {Schriften des Forschungszentrums Jülich IAS Series},
      pages        = {80 - 83},
      year         = {2025},
      comment      = {Proceedings of the 35th Parallel CFD International
                      Conference 2024},
      booktitle     = {Proceedings of the 35th Parallel CFD
                       International Conference 2024},
      abstract     = {GNNs can be applied to any shape or volume represented by a
                      graph, e.g., triangulated shapes, or computational grids.
                      Convolutional filters in GNNs operate on nodes and their
                      neighboring nodes. This allows more efficient training
                      compared to convolutional neural networks (CNNs), whose
                      convolutional filters are rectangular and operate in
                      Cartesian directions. The goal is to predict respiratory
                      system flow features such as air resistance, wall shear
                      stress, and energy flux within the human nasal cavity during
                      inspiration. The initial step involves generating a virtual
                      population through random scaling applied simultaneously to
                      length, width, and height. Three distinct geometries are
                      chosen to generate 297 virtual patients, including an
                      average one based on 35 healthy patients, a Caucasian
                      healthy patient, and an Asian healthy patient. The second
                      part of the talk exposes the preliminary results based on
                      297 patients with physiological observations and discussions
                      on the accuracy result of the GCNN model.},
      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) /
                      HANAMI - Hpc AlliaNce for Applications and supercoMputing
                      Innovation: the Europe - Japan collaboration (101136269) /
                      JLESC - Joint Laboratory for Extreme Scale Computing
                      (JLESC-20150708)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)951733 /
                      G:(EU-Grant)101136269 / G:(DE-Juel1)JLESC-20150708},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.34734/FZJ-2025-02460},
      url          = {https://juser.fz-juelich.de/record/1041828},
}