Modeling chaotic systems: Dynamical equations vs machine learning approach

Tongfeng Weng, Huijie Yang, Jie Zhang, Michael Small

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Chaotic systems are ubiquitous in the real world, but often analytical models remain inaccessible. We find that a machine-learning method known as “reservoir computing” provides an alternative feasible way for modeling chaotic systems rather than conventional dynamical equations. Specifically, we show that recurrence in temporal and spatial scales of the trained reservoir system are indistinguishable from that of an observed chaotic system. Furthermore, by sharing a common signal, dual synchronization between a chaotic system and its learned reservoir system can be achieved successfully. In the same manner, we show that the identical synchronization also emerges on their coupled system. These findings reveal that reservoir computing approach would be excellent candidate for modeling a great variety of chaotic systems.

Original languageEnglish
Article number106452
Number of pages7
JournalCommunications in Nonlinear Science and Numerical Simulation
Volume114
DOIs
Publication statusPublished - Nov 2022

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