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In this paper, we develop a multivariate regression model and a neuralnetwork model to predict the Reynolds number (Re) and Nusselt number inturbulent thermal convection. We compare their predictions with those ofearlier models of convection: Grossmann-Lohse~[Phys. Rev. Lett. \textbf{86},3316 (2001)], revised Grossmann-Lohse~[Phys. Fluids \textbf{33}, 015113(2021)], and Pandey-Verma [Phys. Rev. E \textbf{94}, 053106 (2016)] models. Weobserve that although the predictions of all the models are quite close to eachother, the machine learning models developed in this work provide the bestmatch with the experimental and numerical results.

Predictions of Reynolds and Nusselt numbers in turbulent convection using machine-learning models