A stand-alone machine learned turbulence model is applied for the solution of integral boundary layer equations, and issues and constraints associated with the model are discussed. The results demonstrate that grouping flow variables into a problem relevant parameter for input during machine learning is desirable to improve accuracy of the model. Further, the accuracy of the model can be improved significantly by incorporation of physics-based constraints during training. Data driven machine learning training requires trial-and-error approach, shows oscillations in a posteriori predictions, and shows unphysical results when used with arbitrary initial condition, as the query is essentially extrapolations. Physics informed machine learning addresses the above limitations, and is identified to be a viable approach for development of machine learned turbulence model.

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