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@MISC{Rttgers:1031683,
      author       = {Rüttgers, Mario and Hübenthal, Fabian and Tsubokura,
                      Makoto and Lintermann, Andreas},
      title        = {{D}iploma {B}est {P}aper {A}ward: 15th lnternational
                      {C}onference on {P}arallel {P}rocessing and {A}pplied
                      {M}athematics ({PPAM} 2024)},
      publisher    = {PPAM 2024 Chairs of Program Committee},
      reportid     = {FZJ-2024-05793},
      year         = {2024},
      abstract     = {DIPLOMA: PPAM Besf Paper Award. The lnternational
                      Conference on Parallel Processing and Applied Mathematics
                      (PPAM) Best Paper Award is given upon recommendation of the
                      PPAM Chairs and Program Committee in recognition of the
                      research paper's quality, originality, and significance of
                      the work in high performance computing. The PPAM Best Paper
                      was first awarded at PPAM 2019 in Bialystok. The PPAM 2024
                      Award for Workshop is bestowed to: Mario Rüttgers, Fabian
                      Hübenthal, Makoto Tsubokura, and Andreas Lintermann for
                      their paper "Parallel reinforcement learning and Gaussian
                      process regress for improved physics-based nasal surgery
                      planning".},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / HANAMI - Hpc
                      AlliaNce for Applications and supercoMputing Innovation: the
                      Europe - Japan collaboration (101136269)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)101136269},
      typ          = {PUB:(DE-HGF)38},
      url          = {https://juser.fz-juelich.de/record/1031683},
}