
doi: 10.1063/5.0030651
pmid: 33261366
We observe the presence of infinitely fine-scaled alternations within the performance landscape of reservoir computers aimed for chaotic data forecasting. We investigate the emergence of the observed structures by means of variations of the transversal stability of the synchronization manifold relating the observational and internal dynamical states. Finally, we deduce a simple calibration method in order to attenuate the thus evidenced performance uncertainty.
machine learning, fractals, chaotic systems, Gruppe Komplexe Plasmen, prediction, reservoir computing, time series, artificial intelligence
machine learning, fractals, chaotic systems, Gruppe Komplexe Plasmen, prediction, reservoir computing, time series, artificial intelligence
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