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The article discusses the importance of accurate modeling of hypersonic turbulent boundary layers in predicting surface heat flux for hypersonic vehicles. It also explores the use of machine-learning techniques for developing data-driven turbulence models as an alternative to traditional RANS models. The need for a parameter-sweeping database for testing the predictive generality of these models is highlighted.