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physics.flu-dyn2025
Realizability-Informed Machine Learning for Turbulence Anisotropy Mappings
Ryley McConkey, Nikhila Kalia, Eugene Yee +1
Within the context of machine learning-based closure mappings for RANS turbulence modelling, physical realizability is often enforced using ad-hoc postprocessing of the predicted a…
physics.flu-dyn2025
Kolmogorov-Arnold Networks for Turbulence Anisotropy Mapping
Nikhila Kalia, Ryley McConkey, Eugene Yee +1
This study evaluates the generalization performance and representation efficiency (parsimony) of a previously introduced Tensor Basis Kolmogorov-Arnold Network (TBKAN) architecture…
physics.flu-dyn2025
Bayesian Optimization of the GEKO Turbulence Model for Predicting Flow Separation Over a Smooth Surface
Nikhila Kalia, Ryley McConkey, Eugene Yee +1
This paper applies Bayesian-optimization-RANS (turbo-RANS) to improve Reynolds-averaged Navier-Stokes (RANS) turbulence models for a converging-diverging channel, a case with adver…