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A Kuramoto Model Approach to Predicting Chaotic Systems with Echo State Networks

By Sophie Wu, Jackson Howe, Lyle Muller

Description

Our 2022 paper creates an echo state network (ESN) with a network and activation function based on the Kuramoto Model. Our model successfully predicts the logistic map chaotic system for a nontrivial number of time steps (~30). We comment on the similarity between the KuramotoESN and another widely known physical model, the Ising model.

Requirements

Use Instructions

The /helpers folder contains all files needed to run the scripts figureX.m. The figures are named as they appear in the above mentioned paper.

Acknowledgements

The authors would like to acknowledge Gabriel Benigno, Dr. Roberto Budzinski, Molly, and the members of the Muller Lab for insightful discussion and mentorship, and for providing a welcoming and exciting atmosphere in which we were able to explore this project. The authors extend a special thank you to Dr. Lyle Muller for his mentorship, supervision, and expertise provided throughout the summer. This work is supported by the USRI program at Western and the NSERC USRA program.

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