Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach

doi:10.1103/PhysRevLett.120.024102

. 2018 Jan 12;120(2):024102.

doi: 10.1103/PhysRevLett.120.024102.

Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach

Jaideep Pathak^{1

2}, Brian Hunt^{3

4}, Michelle Girvan^{1

2

3}, Zhixin Lu^{1

3}, Edward Ott^{1

2

5}

Affiliations

¹ Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland 20742, USA.
² Department of Physics, University of Maryland, College Park, Maryland 20742, USA.
³ Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, USA.
⁴ Department of Mathematics, University of Maryland, College Park, Maryland 20742, USA.
⁵ Department of Electrical and Computer Engineering, University of Maryland, Maryland 20742, USA.

PMID: 29376715
DOI: 10.1103/PhysRevLett.120.024102

Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach

Jaideep Pathak et al. Phys Rev Lett. 2018.

. 2018 Jan 12;120(2):024102.

doi: 10.1103/PhysRevLett.120.024102.

Authors

Jaideep Pathak^{1

2}, Brian Hunt^{3

4}, Michelle Girvan^{1

2

3}, Zhixin Lu^{1

3}, Edward Ott^{1

2

5}

Affiliations

¹ Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland 20742, USA.
² Department of Physics, University of Maryland, College Park, Maryland 20742, USA.
³ Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, USA.
⁴ Department of Mathematics, University of Maryland, College Park, Maryland 20742, USA.
⁵ Department of Electrical and Computer Engineering, University of Maryland, Maryland 20742, USA.

PMID: 29376715
DOI: 10.1103/PhysRevLett.120.024102

Abstract

We demonstrate the effectiveness of using machine learning for model-free prediction of spatiotemporally chaotic systems of arbitrarily large spatial extent and attractor dimension purely from observations of the system's past evolution. We present a parallel scheme with an example implementation based on the reservoir computing paradigm and demonstrate the scalability of our scheme using the Kuramoto-Sivashinsky equation as an example of a spatiotemporally chaotic system.

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Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach

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Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach

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