Comparative analysis of modern symbolic regression methods in the identification of dynamic systems based on observational data
Abstract
Comparative analysis of modern symbolic regression methods in the identification of dynamic systems based on observational data
Incoming article date: 17.09.2025In modern research, symbolic regression is a powerful tool for constructing mathematical models of various systems. In this paper, three symbolic regression methods are applied and compared: genetic programming, sparse identification of nonlinear dynamics and hybrid method. The performance of each method is evaluated by its ability to find accurate models with high accuracy and low complexity in the presence of varying levels of noise in the observational data. Based on the results of the experiments, it was concluded that the best method for identifying dynamic systems is the hybrid method, which combines genetic programming and sparse identification
Keywords: symbolic regression,identification of dynamical systems,genetic programming, sparse identification of nonlinear dynamics, hybrid method GP-SINDy