Data-driven cold starting of good reservoirs
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Date
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
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.