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@MISC{Rttgers:1031683,
author = {Rüttgers, Mario and Hübenthal, Fabian and Tsubokura,
Makoto and Lintermann, Andreas},
title = {{D}iploma {B}est {P}aper {A}ward: 15th lnternational
{C}onference on {P}arallel {P}rocessing and {A}pplied
{M}athematics ({PPAM} 2024)},
publisher = {PPAM 2024 Chairs of Program Committee},
reportid = {FZJ-2024-05793},
year = {2024},
abstract = {DIPLOMA: PPAM Besf Paper Award. The lnternational
Conference on Parallel Processing and Applied Mathematics
(PPAM) Best Paper Award is given upon recommendation of the
PPAM Chairs and Program Committee in recognition of the
research paper's quality, originality, and significance of
the work in high performance computing. The PPAM Best Paper
was first awarded at PPAM 2019 in Bialystok. The PPAM 2024
Award for Workshop is bestowed to: Mario Rüttgers, Fabian
Hübenthal, Makoto Tsubokura, and Andreas Lintermann for
their paper "Parallel reinforcement learning and Gaussian
process regress for improved physics-based nasal surgery
planning".},
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)38},
url = {https://juser.fz-juelich.de/record/1031683},
}