Data-driven cold starting of good reservoirs

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Authors

Grigoryeva, Lyudmila
Grigoryeva, Lyudmila
Kemeth, Felix P.
Kevrekidis, Yannis
Manjunath, Gandhi
Ortega, Juan-Pablo
Steynberg, Matthys J.

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier

Abstract

Using short histories of observations from a dynamical system, a workflow for the post-training initialization of reservoir computing systems is described. This strategy is called cold-starting, and it is based on a map called the starting map, which is determined by an appropriately short history of observations that maps to a unique initial condition in the reservoir space. The time series generated by the reservoir system using that initial state can be used to run the system in autonomous mode in order to produce accurate forecasts of the time series under consideration immediately. By utilizing this map, the lengthy ‘‘washouts’’ that are necessary to initialize reservoir systems can be eliminated, enabling the generation of forecasts using any selection of appropriately short histories of the observations.

Description

DATA AVAILABILITY : We included the link to the GitHub folder that contains the code and data used in the paper.

Keywords

Reservoir computing, Generalized synchronization, Starting map, Forecasting, Path continuation, Dynamical systems, SDG-04: Quality education, SDG-06: Clean water and sanitation, SDG-09: Industry, innovation and infrastructure

Sustainable Development Goals

SDG-04:Quality Education
SDG-06:Clean water and sanitation
SDG-09: Industry, innovation and infrastructure

Citation

Grigoryeva, L., Hamzi, B., Kemeth, F.P. et al. 2024, 'Data-driven cold starting of good reservoirs', Physica D, vol. 469, art. 134325, pp. 1-12. https://DOI.org/10.1016/j.physd.2024.134325.