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@INBOOK{Rttgers:1038857,
      author       = {Rüttgers, Mario and Hübenthal, Fabian and Tsubokura,
                      Makoto and Lintermann, Andreas},
      title        = {{P}arallel {R}einforcement {L}earning and {G}aussian
                      {P}rocess {R}egression for {I}mproved {P}hysics-{B}ased
                      {N}asal {S}urgery {P}lanning},
      volume       = {15581},
      address      = {Cham},
      publisher    = {Springer Nature Switzerland},
      reportid     = {FZJ-2025-01673},
      isbn         = {978-3-031-85702-7 (print), 978-3-031-85703-4 (electronic)},
      series       = {Lecture Notes in Computer Science},
      pages        = {79 - 96},
      year         = {2025},
      comment      = {Parallel Processing and Applied Mathematics},
      booktitle     = {Parallel Processing and Applied
                       Mathematics},
      abstract     = {Septoplasty and turbinectomy are among the most frequent
                      but also most debated interventions in the field of
                      rhinology. A previously developed tool enhances surgery
                      planning by physical aspects of respiration, i.e., for the
                      first time a reinforcement learning (RL) algorithm is
                      combined with large-scale computational fluid dynamics (CFD)
                      simulations to plan anti-obstructive surgery. In the current
                      study, an improvement of the tool’s predictive
                      capabilities is investigated for the aforementioned types of
                      surgeries by considering two approaches: (i) training of
                      parallel environments is executed on multiple ranks and the
                      agents of each environment share their experience in a
                      pre-defined interval and (ii) for some of the state-reward
                      combinations the CFD solver is replaced by a Gaussian
                      process regression (GPR) model for an improved computational
                      efficiency. It is found that employing a parallel RL
                      algorithm improves the reliability of the surgery planning
                      tool in finding the global optimum. However, parallel
                      training leads to a larger number of state-reward
                      combinations that need to be computed by the CFD solver.
                      This overhead is compensated by replacing some of the
                      computations with the GPR algorithm, i.e., around $6\%$ of
                      the computations can be saved without significantly
                      degrading the predictions’ accuracy. Nevertheless,
                      increasing the number of state-reward combinations predicted
                      by the GPR algorithm only works to a certain extent, since
                      this also leads to larger errors.},
      month         = {Sep},
      date          = {2024-09-08},
      organization  = {Parallel Processing and Applied
                       Mathematics 2024, Ostrava (Czech
                       Republic), 8 Sep 2024 - 11 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) / 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)8 / PUB:(DE-HGF)7},
      url          = {https://juser.fz-juelich.de/record/1038857},
}