Predicting multi-parametric dynamics of an externally forced oscillator using reservoir computing and minimal data

This study uses a data-driven approach to investigate how bifurcations can be learned from a few system response measurements. Particularly, the concept of reservoir computing (RC) is employed. As proof of concept, a minimal training dataset under the resource constraint problem of a Duffing oscillator with harmonic external forcing is provided as training data. Our results indicate that the RC not only learns to represent the system dynamics for the external forcing seen during training, but it also provides qualitatively accurate and robust system response predictions for completely unknown multi-parameter regimes outside the training data. Particularly, while being trained solely on regular period-2 cycle dynamics, the proposed framework correctly predicts higher-order periodic and even chaotic dynamics for out-of-distribution forcing signals.

Read full article here: M. Yadav, S. Chauhan, M. D. Shrimali and M. Stender, “Predicting multi-parametric dynamics of an externally forced oscillator using reservoir computing and minimal data,” Nonlinear Dynamics, 113, 5977-5990 (2024