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001029111 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-04967
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001029111 041__ $$aEnglish
001029111 1001_ $$0P:(DE-Juel1)187395$$aJohn, Chelsea Maria$$b0$$ufzj
001029111 1112_ $$aPhysML Workshop 2024$$cOslo$$d2024-05-14 - 2024-05-16$$gPhysML$$wNorway
001029111 245__ $$aHarnessing Fourier Neural Operator For Rayleigh–Bénard Convection
001029111 260__ $$c2024
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001029111 520__ $$aRayleigh-Bénard convection, is a classic fluid dynamics problem, with applications in geophysical, astrophysical, and industrial flows. Fourier Neural Operator (FNO) leverages neural networks and Fourier analysis to efficiently model spatiotemporal dynamics in fluid systems, offering a promising avenue for accurate and scalable simulations. In this poster, first results on the application of FNO for tackling the Rayleigh-Benard convection equations is presented.
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001029111 536__ $$0G:(EU-Grant)101118139$$aInno4Scale - Innovative Algorithms for Applications on European Exascale Supercomputers (101118139)$$c101118139$$fHORIZON-EUROHPC-JU-2022-ALG-02$$x2
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001029111 7001_ $$0P:(DE-Juel1)145478$$aHerten, Andreas$$b1$$ufzj
001029111 7001_ $$0P:(DE-Juel1)185654$$aKesselheim, Stefan$$b2$$ufzj
001029111 7001_ $$0P:(DE-HGF)0$$aRuprecht, Daniel$$b3
001029111 8564_ $$uhttps://sites.google.com/view/physmlworkshop24/titles-and-abstracts
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001029111 9141_ $$y2024
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