TY  - CONF
AU  - Rüttgers, Mario
AU  - Hübenthal, Fabian
AU  - Tsubokura, Makoto
AU  - Lintermann, Andreas
TI  - Parallel Reinforcement Learning and Gaussian Process Regression for Improved Physics-Based Nasal Surgery Planning
VL  - 15581
CY  - Cham
PB  - Springer Nature Switzerland
M1  - FZJ-2026-01057
SN  - 978-3-031-85702-7 (print)
T2  - Lecture Notes in Computer Science
SP  - 79 - 96
PY  - 2025
AB  - 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.
T2  - International Conference on Parallel Processing and Applied Mathematics
CY  - 8 Sep 2024 - 11 Sep 2024, Ostrava (Czech Republic)
Y2  - 8 Sep 2024 - 11 Sep 2024
M2  - Ostrava, Czech Republic
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
DO  - DOI:10.1007/978-3-031-85703-4_6
UR  - https://juser.fz-juelich.de/record/1052700
ER  -