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001041828 037__ $$aFZJ-2025-02460
001041828 041__ $$aEnglish
001041828 1001_ $$0P:(DE-HGF)0$$aCalmet, Hadrien$$b0$$eCorresponding author
001041828 1112_ $$a35th Parallel CFD International Conference 2024$$cBonn$$d2024-09-02 - 2024-09-04$$gParCFD 2024$$wGermany
001041828 245__ $$aCreating a Virtual Population of the Human Nasal Cavity for Velocity-Based Predictions of Respiratory Flow Features Using Graph Convolutional Neural Networks
001041828 260__ $$aJülich$$bForschungszentrum Jülich GmbH Zentralbibliothek, Verlag$$c2025
001041828 29510 $$aProceedings of the 35th Parallel CFD International Conference 2024
001041828 300__ $$a80 - 83
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001041828 4900_ $$aSchriften des Forschungszentrums Jülich IAS Series$$v69
001041828 520__ $$aGNNs 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.
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001041828 536__ $$0G:(EU-Grant)951733$$aRAISE - Research on AI- and Simulation-Based Engineering at Exascale (951733)$$c951733$$fH2020-INFRAEDI-2019-1$$x1
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001041828 536__ $$0G:(DE-Juel1)JLESC-20150708$$aJLESC - Joint Laboratory for Extreme Scale Computing (JLESC-20150708)$$cJLESC-20150708$$fJLESC$$x3
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001041828 7001_ $$0P:(DE-HGF)0$$aCalafell, Joan$$b1
001041828 7001_ $$0P:(DE-Juel1)188513$$aSarma, Rakesh$$b2$$ufzj
001041828 7001_ $$0P:(DE-Juel1)177985$$aRüttgers, Mario$$b3$$ufzj
001041828 7001_ $$0P:(DE-Juel1)165948$$aLintermann, Andreas$$b4$$ufzj
001041828 7001_ $$0P:(DE-HGF)0$$aHouzeaux, Guillaume$$b5
001041828 770__ $$z978-3-95806-819-3
001041828 773__ $$a10.34734/FZJ-2025-02460
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