Machine learning (ML), a subset of artificial intelligence, refers to methods that have the ability to “learn” from experience, enabling them to carry out designated tasks. Examples of machine learning tasks are detection, recognition, diagnosis, optimization, and prediction. Machine learning can also often be used in different areas of complex systems research involving identification of the basic system structure (e.g., network nodes and links) and study of the dynamic behavior of nonlinear systems (e.g., determining Lyapunov exponents, prediction of future evolution, and inferring causality of interactions). Conversely, machine learning procedures, such as “reservoir computing” and “long short-term memory”, are often dynamical in nature, and the understanding of when, how, and why they are able to function so well can potentially be addressed using tools from dynamical systems theory. For example, a recent consequence of this has been the realization of new optics-based physical realizations of reservoir computers. In the area...
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June 2020
Research Article|
June 26 2020
Introduction to Focus Issue: When machine learning meets complex systems: Networks, chaos, and nonlinear dynamics
Yang Tang
;
Yang Tang
1
Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology
, Shanghai, China
2
Department of Automation, East China University of Science and Technology
, Shanghai, China
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Jürgen Kurths
;
Jürgen Kurths
a)
3
Potsdam Institute for Climate Impact Research
, Potsdam 14473, Germany
4
Department of Physics, Humboldt University of Berlin
, Berlin 12489, Germany
a)Author to whom correspondence should be addressed: juergen.kurths@pik-potsdam.de
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Wei Lin
;
Wei Lin
5
Center for Computational Systems Biology of ISTBI and Research Institute of Intelligent Complex Systems, Fudan University
, Shanghai 200433, China
6
School of Mathematical Sciences, SCMS, SCAM, and LMNS, Fudan University
, Shanghai 200433, China
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Edward Ott;
Edward Ott
7
Department of Physics, University of Maryland
, College Park, Maryland 20742, USA
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Ljupco Kocarev
Ljupco Kocarev
8
Macedonian Academy of Sciences and Arts
, 1000 Skopje, Macedonia
9
Faculty of Computer Science and Engineering, University “Sv Kiril i Metodij,”
1000 Skopje, Macedonia
10
BioCircuits Institute, University of California San Diego
, La Jolla, California 92093, USA
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a)Author to whom correspondence should be addressed: juergen.kurths@pik-potsdam.de
Note: This paper is part of the Focus Issue, “When Machine Learning Meets Complex Systems: Networks, Chaos and Nonlinear Dynamics.”
Chaos 30, 063151 (2020)
Article history
Received:
June 04 2020
Accepted:
June 05 2020
Citation
Yang Tang, Jürgen Kurths, Wei Lin, Edward Ott, Ljupco Kocarev; Introduction to Focus Issue: When machine learning meets complex systems: Networks, chaos, and nonlinear dynamics. Chaos 1 June 2020; 30 (6): 063151. https://doi.org/10.1063/5.0016505
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