SINDy Algorithm

The Sparse Identification of Nonlinear Dynamics (SINDy) algorithm is a data-driven method for discovering the governing equations of complex systems from observational data, aiming to identify sparse representations of underlying dynamics. Current research focuses on enhancing SINDy's robustness to noise and limitations in data quantity, exploring variations like Nested SINDy for increased expressivity and incorporating neural networks and integral forms for improved accuracy. These advancements are significant for various scientific fields, enabling more efficient and accurate modeling of complex systems across diverse applications, from fluid dynamics to structural mechanics, by providing a powerful tool for data-driven model discovery.