Physics-aware deep neural networks for surrogate modeling of turbulent natural convection

Understand

Recent works have explored the potential of machine learning as data-driven turbulence closures for RANS and LES techniques.

• Beyond these advances, the high expressivity and agility of physics-informed neural networks (PINNs) make them promising candidates for full fluid flow PDE modeling.
• An important question is whether this new paradigm, exempt from the traditional notion of discretization of the underlying operators very much connected to the flow scales resolution, is capable of sustaining high levels of turbulence characterized by multi-scale features? We investigate the use of PINNs surrogate modeling for turbulent Rayleigh-B{\'e}nard (RB) convection flows in rough and smooth rectangular cavities, mainly relying on DNS temperature data from the fluid bulk.
• We carefully quantify the computational requirements under which the formulation is capable of accurately recovering the flow hidden quantities.