guest ::
| Home > Publications database > Creating a Virtual Population of the Human Nasal Cavity for Velocity-Based Predictions of Respiratory Flow Features Using Graph Convolutional Neural Networks > print |
| Home > Publications database > Creating a Virtual Population of the Human Nasal Cavity for Velocity-Based Predictions of Respiratory Flow Features Using Graph Convolutional Neural Networks > print |
| 001 | 1041828 | ||
| 005 | 20250515202216.0 | ||
| 024 | 7 | _ | |a 10.34734/FZJ-2025-02460 |2 datacite_doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2025-02460 |2 doi |
| 037 | _ | _ | |a FZJ-2025-02460 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Calmet, Hadrien |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a 35th Parallel CFD International Conference 2024 |g ParCFD 2024 |c Bonn |d 2024-09-02 - 2024-09-04 |w Germany |
| 245 | _ | _ | |a Creating a Virtual Population of the Human Nasal Cavity for Velocity-Based Predictions of Respiratory Flow Features Using Graph Convolutional Neural Networks |
| 260 | _ | _ | |a Jülich |c 2025 |b Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag |
| 295 | 1 | 0 | |a Proceedings of the 35th Parallel CFD International Conference 2024 |
| 300 | _ | _ | |a 80 - 83 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Paper |2 DataCite |
| 336 | 7 | _ | |a Contribution to a conference proceedings |b contrib |m contrib |0 PUB:(DE-HGF)8 |s 1747300072_12426 |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 Schriften des Forschungszentrums Jülich IAS Series |v 69 |
| 520 | _ | _ | |a GNNs can be applied to any shape or volume represented by a graph, e.g., triangulated shapes, or computational grids. Convolutional filters in GNNs operate on nodes and their neighboring nodes. This allows more efficient training compared to convolutional neural networks (CNNs), whose convolutional filters are rectangular and operate in Cartesian directions. The goal is to predict respiratory system flow features such as air resistance, wall shear stress, and energy flux within the human nasal cavity during inspiration. The initial step involves generating a virtual population through random scaling applied simultaneously to length, width, and height. Three distinct geometries are chosen to generate 297 virtual patients, including an average one based on 35 healthy patients, a Caucasian healthy patient, and an Asian healthy patient. The second part of the talk exposes the preliminary results based on 297 patients with physiological observations and discussions on the accuracy result of the GCNN model. |
| 536 | _ | _ | |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |f POF IV |x 0 |
| 536 | _ | _ | |a RAISE - Research on AI- and Simulation-Based Engineering at Exascale (951733) |0 G:(EU-Grant)951733 |c 951733 |f H2020-INFRAEDI-2019-1 |x 1 |
| 536 | _ | _ | |a HANAMI - Hpc AlliaNce for Applications and supercoMputing Innovation: the Europe - Japan collaboration (101136269) |0 G:(EU-Grant)101136269 |c 101136269 |x 2 |
| 536 | _ | _ | |a JLESC - Joint Laboratory for Extreme Scale Computing (JLESC-20150708) |0 G:(DE-Juel1)JLESC-20150708 |c JLESC-20150708 |f JLESC |x 3 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 700 | 1 | _ | |a Calafell, Joan |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Sarma, Rakesh |0 P:(DE-Juel1)188513 |b 2 |u fzj |
| 700 | 1 | _ | |a Rüttgers, Mario |0 P:(DE-Juel1)177985 |b 3 |u fzj |
| 700 | 1 | _ | |a Lintermann, Andreas |0 P:(DE-Juel1)165948 |b 4 |u fzj |
| 700 | 1 | _ | |a Houzeaux, Guillaume |0 P:(DE-HGF)0 |b 5 |
| 770 | _ | _ | |z 978-3-95806-819-3 |
| 773 | _ | _ | |a 10.34734/FZJ-2025-02460 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1041828/files/172.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:1041828 |p openaire |p open_access |p driver |p VDB |p ec_fundedresources |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)188513 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)177985 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)165948 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5111 |x 0 |
| 914 | 1 | _ | |y 2025 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 980 | _ | _ | |a contrib |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a contb |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|