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RESERVOIR COMPUTING

Predicting hidden structure in dynamical systems

Nature Machine Intelligence volume 3, pages 281–282 (2021)Cite this article

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The dynamical properties of a nonlinear system can be learned from its time-series data, but is it possible to predict what happens when the system is tuned far away from its training values?

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Fig. 1: A reservoir computer is trained on time-series data of a dynamical system — such as a fox–rabbit predator–prey system — below a bifurcation point.

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Author information

Authors and Affiliations

  1. Department of Physics, The Ohio State University, Columbus, OH, USA

    Daniel J. Gauthier

  2. Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (CSIC-UIB), Campus Universitat de les Illes Balears, Palma de Mallorca, Spain

    Ingo Fischer

Authors
  1. Daniel J. Gauthier
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  2. Ingo Fischer
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Corresponding author

Correspondence to Daniel J. Gauthier.

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Competing interests

D.J.G. is a co-founder of ResCon Technologies, LLC, which focuses on commercializing reservoir computers for edge computing applications.

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Gauthier, D.J., Fischer, I. Predicting hidden structure in dynamical systems. Nat Mach Intell 3, 281–282 (2021). https://doi.org/10.1038/s42256-021-00329-8

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