A neural network approach for the solution of Van der Pol-Mathieu-Duffing oscillator model.

The concept of oscillator problems finds its indispensable presence in numerous dynamical systems. Machine learning techniques for handling dynamical systems is a challenging and rapidly expanding field of research. In this regard, a machine learning approach, namely Symplectic Artificial Neural Network model, has been used for handling the non-linear systems arising in dusty plasma models. The primary objective of this article is to investigate, the dynamics of Van der Pol-Mathieu-Duffing Oscillator problems for different excitation functions using the meshfree Symplectic Artificial Neural Network algorithm. The numerical simulations and graphical representations are carried out to establish the accuracy of the presented algorithm. Also, the obtained simulation results are compared with the existing numerical solutions. In addition, the statistical assessment studies at various testing points confirm an excellent agreement between the present simulation results and the existing results. [ABSTRACT FROM AUTHOR]