Introduction to focus issue: Synchronization in large networks and continuous media—data, models, and supermodels
The synchronization of loosely coupled chaotic systems has increasingly found applications to large networks of differential equations and to models of continuous media. These applications are at the core of the present Focus Issue. Synchronization between a system and its model, based on limited ob...
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| Veröffentlicht in: | Chaos (Woodbury, N.Y.) N.Y.), 2017-12, Vol.27 (12), p.126601-126601 |
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| container_issue | 12 |
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| container_title | Chaos (Woodbury, N.Y.) |
| container_volume | 27 |
| creator | Duane, Gregory S. Grabow, Carsten Selten, Frank Ghil, Michael |
| description | The synchronization of loosely coupled chaotic systems has increasingly found
applications to large networks of differential equations and to models of continuous
media. These applications are at the core of the present Focus Issue. Synchronization
between a system and its model, based on limited observations, gives a new perspective on
data assimilation. Synchronization among different models of the same system defines a
supermodel that can achieve partial consensus among models that otherwise disagree in
several respects. Finally, novel methods of time series analysis permit a better
description of synchronization in a system that is only observed partially and for a
relatively short time. This Focus Issue discusses synchronization in extended systems or
in components thereof, with particular attention to data assimilation, supermodeling, and
their applications to various areas, from climate modeling to macroeconomics. |
| doi_str_mv | 10.1063/1.5018728 |
| format | Article |
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applications to large networks of differential equations and to models of continuous
media. These applications are at the core of the present Focus Issue. Synchronization
between a system and its model, based on limited observations, gives a new perspective on
data assimilation. Synchronization among different models of the same system defines a
supermodel that can achieve partial consensus among models that otherwise disagree in
several respects. Finally, novel methods of time series analysis permit a better
description of synchronization in a system that is only observed partially and for a
relatively short time. This Focus Issue discusses synchronization in extended systems or
in components thereof, with particular attention to data assimilation, supermodeling, and
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applications to large networks of differential equations and to models of continuous
media. These applications are at the core of the present Focus Issue. Synchronization
between a system and its model, based on limited observations, gives a new perspective on
data assimilation. Synchronization among different models of the same system defines a
supermodel that can achieve partial consensus among models that otherwise disagree in
several respects. Finally, novel methods of time series analysis permit a better
description of synchronization in a system that is only observed partially and for a
relatively short time. This Focus Issue discusses synchronization in extended systems or
in components thereof, with particular attention to data assimilation, supermodeling, and
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applications to large networks of differential equations and to models of continuous
media. These applications are at the core of the present Focus Issue. Synchronization
between a system and its model, based on limited observations, gives a new perspective on
data assimilation. Synchronization among different models of the same system defines a
supermodel that can achieve partial consensus among models that otherwise disagree in
several respects. Finally, novel methods of time series analysis permit a better
description of synchronization in a system that is only observed partially and for a
relatively short time. This Focus Issue discusses synchronization in extended systems or
in components thereof, with particular attention to data assimilation, supermodeling, and
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| fulltext | fulltext |
| identifier | ISSN: 1054-1500 |
| ispartof | Chaos (Woodbury, N.Y.), 2017-12, Vol.27 (12), p.126601-126601 |
| issn | 1054-1500 1089-7682 |
| language | eng |
| recordid | cdi_pubmed_primary_29289046 |
| source | American Institute of Physics (AIP) Journals; Alma/SFX Local Collection |
| subjects | Chaos theory Climate models Differential equations Economic analysis Economic models Synchronism Time series Time synchronization |
| title | Introduction to focus issue: Synchronization in large networks and continuous media—data, models, and supermodels |
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