%0 Book Section
%A Rüttgers, Mario
%A Hübenthal, Fabian
%A Tsubokura, Makoto
%A Lintermann, Andreas
%T Parallel Reinforcement Learning and Gaussian Process Regression for Improved Physics-Based Nasal Surgery Planning
%V 15581
%C Cham
%I Springer Nature Switzerland
%M FZJ-2025-01673
%@ 978-3-031-85702-7 (print), 978-3-031-85703-4 (electronic)
%B Lecture Notes in Computer Science
%P 79 - 96
%D 2025
%< Parallel Processing and Applied Mathematics
%X 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.
%B Parallel Processing and Applied Mathematics 2024
%C 8 Sep 2024 - 11 Sep 2024, Ostrava (Czech Republic)
Y2 8 Sep 2024 - 11 Sep 2024
M2 Ostrava, Czech Republic
%F PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
%9 Contribution to a conference proceedingsContribution to a book
%U https://juser.fz-juelich.de/record/1038857