MODELING OF DYNAMICAL SYSTEMS THROUGH MACHINE LEARNING

This review presents the key challenges of discovering dynamics from data and finding data-driven representations that make nonlinear systems amenable to linear analysis. Data-driven models drive to discover the governing equations and give laws of physics. The identification of dynamical systems through machine learning techniques succeeds in inferring physical systems. The two chief challenges are nonlinear dynamics and unknown or partially known dynamics. Machine learning is providing new and powerful techniques for both challenges. Dimensionality reduction methods are used for projecting dynamical methods in reduced form and these methods perform computational efficiency on real-world data.