Emerging opportunities and challenges for the future of reservoir computing

Min Yan, Can Huang (), Peter Bienstman, Peter Tino, Wei Lin and Jie Sun ()
Additional contact information
Min Yan: Huawei Technologies Co. Ltd.
Can Huang: Huawei Technologies Co. Ltd.
Peter Bienstman: Ghent University
Peter Tino: The University of Birmingham
Wei Lin: Fudan University
Jie Sun: Huawei Technologies Co. Ltd.

Nature Communications, 2024, vol. 15, issue 1, 1-18

Abstract: Abstract Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn spatiotemporal features and hidden patterns in complex time series. Shown to have the potential of achieving higher-precision prediction in chaotic systems, those pioneering works led to a great amount of interest and follow-ups in the community of nonlinear dynamics and complex systems. To unlock the full capabilities of reservoir computing towards a fast, lightweight, and significantly more interpretable learning framework for temporal dynamical systems, substantially more research is needed. This Perspective intends to elucidate the parallel progress of mathematical theory, algorithm design and experimental realizations of reservoir computing, and identify emerging opportunities as well as existing challenges for large-scale industrial adoption of reservoir computing, together with a few ideas and viewpoints on how some of those challenges might be resolved with joint efforts by academic and industrial researchers across multiple disciplines.

Date: 2024
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DOI: 10.1038/s41467-024-45187-1

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