Using machine learning to predict statistical properties of non-stationary dynamical processes: System climate,regime transitions, and the effect of stochasticity-Reference-Cited by-同舟云学术

Using machine learning to predict statistical properties of non-stationary dynamical processes: System climate,regime transitions, and the effect of stochasticity

Published:2021-03 Issue:3 Volume:31 Page:033149
ISSN:1054-1500
Container-title:Chaos: An Interdisciplinary Journal of Nonlinear Science
language:en
Short-container-title:Chaos

Author:

Patel Dhruvit¹^ORCID,Canaday Daniel²,Girvan Michelle¹,Pomerance Andrew²,Ott Edward¹

Affiliation:

1. The Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland 26742, USA

2. Potomac Research LLC, Alexandria, Virginia 22311-1311, USA

Funder

National Science Foundation

Defense Advanced Research Projects Agency

Publisher

AIP Publishing

Subject

Applied Mathematics,General Physics and Astronomy,Mathematical Physics,Statistical and Nonlinear Physics

Link

https://aip.scitation.org/doi/am-pdf/10.1063/5.0042598

Reference82 articles.

1. Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication

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

3. Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model

4. Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data

5. Attractor reconstruction by machine learning

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