Connecting reservoir computing with statistical forecasting and deep neural networks

Lina Jaurigue () and Kathy Lüdge ()
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Lina Jaurigue: Institut für Theoretische Physik
Kathy Lüdge: Institut für Physik

Nature Communications, 2022, vol. 13, issue 1, 1-3

Abstract: Standfirst Among the existing machine learning frameworks, reservoir computing demonstrates fast and low-cost training, and its suitability for implementation in various physical systems. This Comment reports on how aspects of reservoir computing can be applied to classical forecasting methods to accelerate the learning process, and highlights a new approach that makes the hardware implementation of traditional machine learning algorithms practicable in electronic and photonic systems.

Date: 2022
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DOI: 10.1038/s41467-021-27715-5

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