Reviews linear and nonlinear SciML surrogates for coupled fluid flow and transport, with new PINN modeling of turbidity currents and β-VAE mode extraction from Rayleigh-Bénard convection.
Journal of Fluid Mechanics204, 1–30 (1989)
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Cryogenic RBC heat-transfer data require rigorous uncertainty analysis to separate possible ultimate-regime transitions from non-Oberbeck-Boussinesq effects and experimental imperfections.
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Advances in Scientific Machine Learning for Coupled Fluid Flow and Transport
Reviews linear and nonlinear SciML surrogates for coupled fluid flow and transport, with new PINN modeling of turbidity currents and β-VAE mode extraction from Rayleigh-Bénard convection.
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Experimental Challenges in Determining Heat Transfer Efficiency Scaling in Highly Turbulent Cryogenic Rayleigh-Benard Convection
Cryogenic RBC heat-transfer data require rigorous uncertainty analysis to separate possible ultimate-regime transitions from non-Oberbeck-Boussinesq effects and experimental imperfections.