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| Home > Publications database > Parallel Reinforcement Learning and Gaussian Process Regression for Improved Physics-Based Nasal Surgery Planning > print |
| 001 | 1052700 | ||
| 005 | 20260127203443.0 | ||
| 020 | _ | _ | |a 978-3-031-85702-7 (print) |
| 020 | _ | _ | |a 978-3-031-85703-4 (electronic) |
| 024 | 7 | _ | |a 10.1007/978-3-031-85703-4_6 |2 doi |
| 024 | 7 | _ | |a 0302-9743 |2 ISSN |
| 024 | 7 | _ | |a 1611-3349 |2 ISSN |
| 024 | 7 | _ | |a 10.34734/FZJ-2026-01057 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2026-01057 |
| 100 | 1 | _ | |a Rüttgers, Mario |0 P:(DE-Juel1)177985 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a International Conference on Parallel Processing and Applied Mathematics |g PPAM 2024 |c Ostrava |d 2024-09-08 - 2024-09-11 |w Czech Republic |
| 245 | _ | _ | |a Parallel Reinforcement Learning and Gaussian Process Regression for Improved Physics-Based Nasal Surgery Planning |
| 260 | _ | _ | |a Cham |c 2025 |b Springer Nature Switzerland |
| 295 | 1 | 0 | |a Parallel Processing and Applied Mathematics |
| 300 | _ | _ | |a 79 - 96 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 336 | 7 | _ | |a Contribution to a conference proceedings |b contrib |m contrib |0 PUB:(DE-HGF)8 |s 1769497552_21828 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Contribution to a book |0 PUB:(DE-HGF)7 |2 PUB:(DE-HGF) |m contb |
| 490 | 0 | _ | |a Lecture Notes in Computer Science |v 15581 |
| 520 | _ | _ | |a 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. |
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| 700 | 1 | _ | |a Hübenthal, Fabian |0 0009-0000-7159-8220 |b 1 |
| 700 | 1 | _ | |a Tsubokura, Makoto |0 0000-0001-6555-9575 |b 2 |
| 700 | 1 | _ | |a Lintermann, Andreas |0 P:(DE-Juel1)165948 |b 3 |
| 770 | _ | _ | |z 978-3-031-85702-7=978-3-031-85703-4 |
| 773 | _ | _ | |a 10.1007/978-3-031-85703-4_6 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1052700/files/978-3-031-85703-4_6.pdf |y OpenAccess |
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