TY  - CONF
AU  - John, Chelsea Maria
AU  - Herten, Andreas
AU  - Kesselheim, Stefan
AU  - Ruprecht, Daniel
TI  - Harnessing Fourier Neural Operator For Rayleigh–Bénard Convection
PB  - TUHH
M1  - FZJ-2024-04967
PY  - 2024
AB  - 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.
T2  - PhysML Workshop 2024
CY  - 14 May 2024 - 16 May 2024, Oslo (Norway)
Y2  - 14 May 2024 - 16 May 2024
M2  - Oslo, Norway
LB  - PUB:(DE-HGF)24
DO  - DOI:10.34734/FZJ-2024-04967
UR  - https://juser.fz-juelich.de/record/1029111
ER  -