Year: 2025
Type: article
Abstract: Mathematical modeling is a powerful tool for describing, predicting, and understanding complex phenomena exhibited by real-world systems. However, identifying the equations that govern a system’s dynamics from experimental data remains a significant challenge without a definitive solution. In this study, evolutionary computing techniques are presented to estimate the governing equations of a dynamical system using time-series data. The main approach is to propose a candidate functions with unknown coefficients, and subsequently perform a parametric estimation using genetic algorithms. Some of the main contributions of the present study are an adequate modification of the genetic algorithm to remove terms with minimal contributions, and a mechanism to escape local optima during the search. To evaluate the proposed method, we applied it to three dynamical systems: a linear model, a nonlinear model, and the Lorenz system. Our results demonstrate a reconstruction with an integral square error below 0.22 and a coefficient of determination $$R^2$$ R 2 of 0.99 for all systems, indicating successful reconstruction of the governing dynamic equations.
Source: Engineering With Computers
Institution: Universidad Nacional Autónoma de México
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,190
Awards: G554419105, G6328215162
OpenAlex ID: W4409675939