Reinforcement learning is emerging as a potent alternative for developing such closures as it requires only low-order statistics and leads to stable closures.
There is growing interest in discovering interpretable, closed-form equations for subgrid-scale (SGS) closures/parameterizations of complex processes in Earth system.
Models of many engineering and natural systems are imperfect.
We design a physics-aware auto-encoder to specifically reduce the dimensionality of solutions arising from convection-dominated nonlinear physical systems.
Foundations of a new projection-based model reduction approach for convection dominated nonlinear fluid flows are summarized.