Examining the intersection of machine learning and computational fluid dynamics — including physics-informed neural networks (PINNs), symbolic regression for turbulence model discovery, surrogate models, reinforcement learning for flow control, and data-driven closure modelling. Covers what these methods genuinely offer and where they fall short.
