Data-driven modeling and parameter estimation of Nonlinear systems

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Publication date: 31 May 2023

Abstract: Nonlinear systems are prevalent in many fields of science and engineering, and understanding their behavior is essential for developing effective control and prediction strategies. In this paper, we present a novel data-driven approach for accurately modeling and estimating parameters of nonlinear systems using trust region optimization. Our method is applied to three classic systems: the Van der Pol oscillator, the Damped oscillator, and the Lorenz system, which have broad applications in various fields, including engineering, physics, and biology. Our results demonstrate that our approach can accurately identify the parameters of these nonlinear systems, providing a reliable characterization of their behavior. We show that the ability to capture the dynamics on the attractor is crucial for these systems, especially in chaotic systems like the Lorenz system. Overall, this article presents a robust data-driven approach for parameter estimation of nonlinear dynamical systems, with promising potential for real-world applications.

Has companion code repository: https://github.com/kaushalkumarsimmons/parameter_estimation2

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