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  Perspectives on adaptive dynamical systems

Sawicki, J., Berner, R., Loos, S., Anvari, M., Bader, R., Barfuss, W., Botta, N., Brede, N., Franović, I., Gauthier, D. J., Goldt, S., Hajizadeh, A., Hövel, P., Karin, O., Lorenz-Spreen, P., Miehl, C., Mölter, J., Olmi, S., Schöll, E., Seif, A., Tass, P. A., Volpe, G., Yanchuk, S., Kurths, J. (2023): Perspectives on adaptive dynamical systems. - Chaos, 33, 7, 071501.
https://doi.org/10.1063/5.0147231

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 Creators:
Sawicki, Jakub1, Author              
Berner, Rico2, Author
Loos, Sarah2, Author
Anvari, Mehrnaz1, Author              
Bader, Rolf2, Author
Barfuss, Wolfram1, Author              
Botta, Nicola1, Author              
Brede, Nuria1, Author              
Franović, Igor2, Author
Gauthier, Daniel J.2, Author
Goldt, Sebastian2, Author
Hajizadeh, Aida2, Author
Hövel, Philipp2, Author
Karin, Omer2, Author
Lorenz-Spreen, Philipp2, Author
Miehl, Christoph2, Author
Mölter, Jan2, Author
Olmi, Simona2, Author
Schöll, Eckehard1, Author              
Seif, Alireza2, Author
Tass, Peter A.2, AuthorVolpe, Giovanni2, AuthorYanchuk, Serhiy1, Author              Kurths, Jürgen1, Author               more..
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Abstract: Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems, such as the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges and give perspectives on future research directions, looking to inspire interdisciplinary approaches.

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Language(s): eng - English
 Dates: 2023-07-242023-07-24
 Publication Status: Finally published
 Pages: 42
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1063/5.0147231
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Machine Learning
Model / method: Nonlinear Data Analysis
Working Group: Network- and machine-learning-based prediction of extreme events
OATYPE: Hybrid - American Institute of Physics
 Degree: -

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Title: Chaos
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 33 (7) Sequence Number: 071501 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/180808