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001041827 005__ 20250512202214.0
001041827 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-02459
001041827 037__ $$aFZJ-2025-02459
001041827 041__ $$aEnglish
001041827 1001_ $$0P:(DE-Juel1)188513$$aSarma, Rakesh$$b0$$eCorresponding author$$ufzj
001041827 1112_ $$a35th Parallel CFD International Conference 2024$$cBonn$$d2024-09-02 - 2024-09-04$$gParCFD 2024$$wGermany
001041827 245__ $$aPredicting Turbulent Boundary Layer Flows Using Transformers Coupled to the Multi-Physics Simulation Tool m-AIA
001041827 260__ $$aJülich$$bForschungszentrum Jülich GmbH Zentralbibliothek, Verlag$$c2025
001041827 29510 $$aProceedings of the 35th Parallel CFD International Conference 2024
001041827 300__ $$a76 - 79
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001041827 4900_ $$aSchriften des Forschungszentrums Jülich IAS Series$$v69
001041827 520__ $$aTime-marching of turbulent flow fields is computationally expensive with traditional numerical solvers. In this regard, transformer neural network, which has been largely successful in many other technical and scientific domains, can potentially predict complex flow fields faster compared to physics-based solvers. In this study, a transformer model is trained for a turbulent boundary layer problem, which is then coupled to the multi-physics solver m-AIA to make predictions of velocity fields. The method can potentially contribute to significant reduction in computational effort while maintaining high accuracy.
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001041827 536__ $$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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001041827 7001_ $$0P:(DE-HGF)0$$aHübenthal, Fabian$$b1
001041827 7001_ $$0P:(DE-HGF)0$$aOrland, Fabian$$b2
001041827 7001_ $$0P:(DE-HGF)0$$aTerboven, Christian$$b3
001041827 7001_ $$0P:(DE-Juel1)165948$$aLintermann, Andreas$$b4$$ufzj
001041827 770__ $$z978-3-95806-819-3
001041827 773__ $$a10.34734/FZJ-2025-02459
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