Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges

arXiv:2306.05014v2 Announce Type: replace-cross
Abstract: There is growing interest in discovering interpretable, closed-form equations for subgrid-scale (SGS) closures/parameterizations of complex processes in Earth systems. Here, we apply a common equation-discovery technique with expansive libraries to learn closures from filtered direct numerical simulations of 2D turbulence and Rayleigh-B\'enard convection (RBC). Across common filters (e.g., Gaussian, box), we robustly discover closures of the same form for momentum and heat fluxes. These closures depend on nonlinear combinations of gradients of filtered variables, with …

abstract apply arxiv challenges closures cs.lg data discovery earth equation fidelity form learn libraries numerical physics.ao-ph physics.flu-dyn processes scale sgs simulations systems turbulence type