  In this paper, we extend Grossmann and Lohse's (GL) model [Phys. Rev. Lett.
{\bf 86}, 3316 (2001)] for the predictions of Reynolds number (Re) and Nusselt
number (Nu) in turbulent Rayleigh-B\'{e}nard convection (RBC). Towards this
objective, we use functional forms for the prefactors of the dissipation rates
in the bulk and the boundary layers. The functional forms arise due to
inhibition of nonlinear interactions in the presence of walls and buoyancy
compared to free turbulence, along with a deviation of viscous boundary layer
profile from Prandtl-Blasius theory. We perform 60 numerical runs on a
three-dimensional unit box for a range of Rayleigh numbers (Ra) and Prandtl
numbers (Pr) and determine the aforementioned functional forms using machine
learning. The revised predictions are in better agreement with the past
numerical and experimental results than those of the GL model, especially for
extreme Prandtl numbers.
