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The continuous development of machine learning algorithms has stimulated the technological revolution on turbulence modeling for Reynolds-averaged Navier–Stokes (RANS) simulations. In this paper, a computable transition-enabled turbulence model is developed with the aid of an artificial neural network (ANN), which maps the relation between the mean flow variables from the shear stress transport (SST) model on coarse grid and the turbulent viscosity from SST- γ model on fine grid. It turns out that the ANN model can predict the aerodynamic integral quantities, transition onset location, laminar separation bubble structure, streamwise velocity distribution, etc. with superior accuracy and robustness for subsonic and moderate transonic airfoil flows. Meanwhile, the ANN model can significantly improve the convergence property with a much higher convergence speed of the model equation in comparison with traditional transition-enabled RANS model. Moreover, due to the lack of solving RANS model equations and use of grid interpolation technology, the computation speed of this ANN model is improved by a factor of about six over the conventional benchmark model. Therefore, the present computable ANN model provides a new perspective for high-efficiency simulation of transition-characterized flows in engineering applications. • A new transition-turbulence modeling strategy is suggested based on artificial neural network (ANN). • The ANN algorithm provides a corrector for the eddy viscosity in full-turbulence model. • The ANN model is intended to mimic the eddy viscosity in transition-enabled model. • No turbulence model equations need to be solved while retaining a good generalization property. • Grid interpolation and frozen correction help improve the computation efficiency while ensuring the simulation accuracy. [ABSTRACT FROM AUTHOR]