Explore the application of sparse identification of nonlinear dynamics (SINDy) algorithm in developing reduced-order models for complex fluid flows. Delve into recent innovations in modeling various flow fields, including bluff body wakes, cavity flows, thermal and electro convection, and magnetohydrodynamics. Learn about the balance between accuracy and efficiency in these models, essential for real-time control, prediction, and optimization of engineering systems involving working fluids. Examine the integration of SINDy with deep autoencoders, Galerkin regression, and stochastic modeling for turbulence. Discover how these techniques are applied to interpret and generalize machine learning in fluid dynamics, from partial differential equation discovery to dominant balance physics modeling.