oRANS: Online optimisation of RANS machine learning models with embedded high-fidelity data generation

Deep learning (DL) has shown promise for flow simulations, but progress is constrained by the scarcity of high-fidelity training data, which is costly to generate and limited to few flow conditions. Closures trained offline on such data tend to overfit and fail to generalise to new regimes. We intro