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001     1029111
005     20250822121412.0
024 7 _ |a 10.34734/FZJ-2024-04967
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037 _ _ |a FZJ-2024-04967
041 _ _ |a English
100 1 _ |a John, Chelsea Maria
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111 2 _ |a PhysML Workshop 2024
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|d 2024-05-14 - 2024-05-16
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245 _ _ |a Harnessing Fourier Neural Operator For Rayleigh–Bénard Convection
260 _ _ |c 2024
336 7 _ |a Conference Paper
|0 33
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336 7 _ |a INPROCEEDINGS
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520 _ _ |a Rayleigh-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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700 1 _ |a Herten, Andreas
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700 1 _ |a Kesselheim, Stefan
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700 1 _ |a Ruprecht, Daniel
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856 4 _ |u https://sites.google.com/view/physmlworkshop24/titles-and-abstracts
856 4 _ |u https://juser.fz-juelich.de/record/1029111/files/FNO_RBC_poster.pdf
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