Even after incorporating:
► Pope tensor invariants
► Wall-distance features
► Curvature and strain/rotation balance
► Non-dimensional force ratios (production/dissipation, etc.)
…the model still struggles in regions where the flow “history” seems to matter more than the local state.
It increasingly feels like:
Turbulence in separated flows is not purely local — transport and history play a critical role.
So I’m exploring directions like:
◉ Non-local / stencil-based neural closures
◉ Streamline-aware or transport-aware features
◉ Differential (evolution-based) closures instead of purely algebraic ones.
I would like to know what is the best practise reseachers use here.
🌍 Looking ahead
I’m also interested in extending similar modelling ideas to micro-scale processes in atmospheric science — such as:
● Cloud microphysics (condensation, evaporation)
● Radiation–turbulence interactions
● Sub-grid process modelling in weather systems
These problems also seem inherently non-local and non-equilibrium, much like separated turbulent flows.
🤝 Open to insights
Would love to hear thoughts from researchers and practitioners working on:
◆ Data-driven RANS / LES closures
◆ Physics-informed ML for turbulence
◆ Atmospheric or multi-physics modelling
Your guidance, experiences, or even failure cases would be extremely valuable.