Poster (Other)

FZJ-2024-04967

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Harnessing Fourier Neural Operator For Rayleigh–Bénard Convection

John, C. M.FZJ* ; Herten, A.FZJ* ; Kesselheim, S.FZJ* ; Ruprecht, D.

2024

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Please use a persistent id in citations: doi:10.34734/FZJ-2024-04967

Abstract: 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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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)
2. 5121 - Supercomputing & Big Data Facilities (POF4-512) (POF4-512)
3. Inno4Scale - Innovative Algorithms for Applications on European Exascale Supercomputers (101118139) (101118139)

Appears in the scientific report 2024

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Database coverage:

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