arXiv Machine Learning By Chelsea Maria John, Thibaut Lunet, Sebastian G\"otschel, Andreas Herten, Stefan Kesselheim, Daniel Ruprecht

Fourier Neural Operators for Rayleigh-B\'enard Convection

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arXiv:2607. 02088v1 Announce Type: new Abstract: We propose an improved Fourier Neural Operator (FNO) for modeling two-dimensional Rayleigh-B\'enard convection by predicting time increments instead of full solutions, achieving higher accuracy than a standard FNO baseline.

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This chapter reviews recent advances in Scientific Machine Learning (SciML) for modeling coupled fluid flow and transport phenomena governed by the incompressible Navier-Stokes and scalar transport equations. Such systems, found in applications like turbidity currents and thermal convection, feature strong nonlinear coupling and multiscale behavior that make high-fidelity simulations computationally expensive.