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@INPROCEEDINGS{Rttgers:1052700,
      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-2026-01057},
      isbn         = {978-3-031-85702-7 (print)},
      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 frequentbut
                      also most debated interventions in the field of rhinology. A
                      previouslydeveloped tool enhances surgery planning by
                      physical aspects of respi-ration, i.e., for the first time a
                      reinforcement learning (RL) algorithm iscombined with
                      large-scale computational fluid dynamics (CFD) simula-tions
                      to plan anti-obstructive surgery. In the current study, an
                      improve-ment of the tool’s predictive capabilities is
                      investigated for the afore-mentioned types of surgeries by
                      considering two approaches: (i) trainingof parallel
                      environments is executed on multiple ranks and the agentsof
                      each environment share their experience in a pre-defined
                      interval and(ii) for some of the state-reward combinations
                      the CFD solver is replacedby a Gaussian process regression
                      (GPR) model for an improved compu-tational efficiency. It is
                      found that employing a parallel RL algorithmimproves the
                      reliability of the surgery planning tool in finding the
                      globaloptimum. However, parallel training leads to a larger
                      number of state-reward combinations that need to be computed
                      by the CFD solver. Thisoverhead is compensated by replacing
                      some of the computations withthe GPR algorithm, i.e., around
                      $6\%$ of the computations can be savedwithout significantly
                      degrading the predictions’ accuracy.
                      Nevertheless,increasing the number of state-reward
                      combinations predicted by theGPR algorithm only works to a
                      certain extent, since this also leads tolarger errors.},
      month         = {Sep},
      date          = {2024-09-08},
      organization  = {International Conference on Parallel
                       Processing and Applied Mathematics,
                       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) / SDLFSE - SDL Fluids
                      $\&$ Solids Engineering (SDLFSE) / HANAMI - Hpc AlliaNce for
                      Applications and supercoMputing Innovation: the Europe -
                      Japan collaboration (101136269)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel-1)SDLFSE /
                      G:(EU-Grant)101136269},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1007/978-3-031-85703-4_6},
      url          = {https://juser.fz-juelich.de/record/1052700},
}