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We present a machine learning–based framework for blending data-driven turbulent closures in the Reynolds-Averaged Navier–Stokes (RANS) equations, aimed at improving their generalizability across diverse flow regimes. Specialized models (hereafter referred to as “experts”) are trained via sparse Bayesian learning and symbolic regression for distinct flow classes, including turbulent ...
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