Symbolic regression for identification prediction and control of dynamical systems In the present work we use symbolic regression for automated modeling of dynamical systems. Symbolic regression is a powerful and general method suitable for data driven identification of mathematical expressions. In particular the structure and parameters of those expressions are identified simultaneously. We consider two main variants of symbolic regression: sparse regression based and genetic programming based symbolic regression. Both are applied to identification prediction and control of dynamical systems. We introduce a new methodology for the data driven identification of nonlinear dynamics for systems undergoing abrupt changes. Building on a sparse regression algorithm derived earlier the model after the change is defined as a minimum update with respect to a reference model of the system identified prior to the change. The technique is successfully exemplified on the chaotic Lorenz system and the van der Pol oscillator. Issues such as computational complexity robustness against noise and requirements with respect to…