| Title: |
Global forecasts in reservoir computers |
| Authors: |
Harding, S.; Leishman, Q.; Lunceford, W.; Passey, D. J.; Pool, T.; Webb, B. |
| Contributors: |
National Science Foundation |
| Source: |
Chaos: An Interdisciplinary Journal of Nonlinear Science ; volume 34, issue 2 ; ISSN 1054-1500 1089-7682 |
| Publisher Information: |
AIP Publishing |
| Publication Year: |
2024 |
| Description: |
A reservoir computer is a machine learning model that can be used to predict the future state(s) of time-dependent processes, e.g., dynamical systems. In practice, data in the form of an input-signal are fed into the reservoir. The trained reservoir is then used to predict the future state of this signal. We develop a new method for not only predicting the future dynamics of the input-signal but also the future dynamics starting at an arbitrary initial condition of a system. The systems we consider are the Lorenz, Rossler, and Thomas systems restricted to their attractors. This method, which creates a global forecast, still uses only a single input-signal to train the reservoir but breaks the signal into many smaller windowed signals. We examine how well this windowed method is able to forecast the dynamics of a system starting at an arbitrary point on a system’s attractor and compare this to the standard method without windows. We find that the standard method has almost no ability to forecast anything but the original input-signal while the windowed method can capture the dynamics starting at most points on an attractor with significant accuracy. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1063/5.0181694 |
| DOI: |
10.1063/5.0181694/19694781/023136_1_5.0181694.pdf |
| Availability: |
https://doi.org/10.1063/5.0181694; https://pubs.aip.org/aip/cha/article-pdf/doi/10.1063/5.0181694/19694781/023136_1_5.0181694.pdf |
| Accession Number: |
edsbas.F7FC4093 |
| Database: |
BASE |