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How to test for partially predictable chaos
For a chaotic system pairs of initially close-by trajectories become eventually fully uncorrelated on the attracting set. This process of decorrelation can split into an initial exponential decrease and a subsequent diffusive process on the chaotic attractor causing the final loss of predictability....
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5430683/ https://www.ncbi.nlm.nih.gov/pubmed/28439074 http://dx.doi.org/10.1038/s41598-017-01083-x |
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author | Wernecke, Hendrik Sándor, Bulcsú Gros, Claudius |
author_facet | Wernecke, Hendrik Sándor, Bulcsú Gros, Claudius |
author_sort | Wernecke, Hendrik |
collection | PubMed |
description | For a chaotic system pairs of initially close-by trajectories become eventually fully uncorrelated on the attracting set. This process of decorrelation can split into an initial exponential decrease and a subsequent diffusive process on the chaotic attractor causing the final loss of predictability. Both processes can be either of the same or of very different time scales. In the latter case the two trajectories linger within a finite but small distance (with respect to the overall extent of the attractor) for exceedingly long times and remain partially predictable. Standard tests for chaos widely use inter-orbital correlations as an indicator. However, testing partially predictable chaos yields mostly ambiguous results, as this type of chaos is characterized by attractors of fractally broadened braids. For a resolution we introduce a novel 0–1 indicator for chaos based on the cross-distance scaling of pairs of initially close trajectories. This test robustly discriminates chaos, including partially predictable chaos, from laminar flow. Additionally using the finite time cross-correlation of pairs of initially close trajectories, we are able to identify laminar flow as well as strong and partially predictable chaos in a 0–1 manner solely from the properties of pairs of trajectories. |
format | Online Article Text |
id | pubmed-5430683 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-54306832017-05-16 How to test for partially predictable chaos Wernecke, Hendrik Sándor, Bulcsú Gros, Claudius Sci Rep Article For a chaotic system pairs of initially close-by trajectories become eventually fully uncorrelated on the attracting set. This process of decorrelation can split into an initial exponential decrease and a subsequent diffusive process on the chaotic attractor causing the final loss of predictability. Both processes can be either of the same or of very different time scales. In the latter case the two trajectories linger within a finite but small distance (with respect to the overall extent of the attractor) for exceedingly long times and remain partially predictable. Standard tests for chaos widely use inter-orbital correlations as an indicator. However, testing partially predictable chaos yields mostly ambiguous results, as this type of chaos is characterized by attractors of fractally broadened braids. For a resolution we introduce a novel 0–1 indicator for chaos based on the cross-distance scaling of pairs of initially close trajectories. This test robustly discriminates chaos, including partially predictable chaos, from laminar flow. Additionally using the finite time cross-correlation of pairs of initially close trajectories, we are able to identify laminar flow as well as strong and partially predictable chaos in a 0–1 manner solely from the properties of pairs of trajectories. Nature Publishing Group UK 2017-04-24 /pmc/articles/PMC5430683/ /pubmed/28439074 http://dx.doi.org/10.1038/s41598-017-01083-x Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Wernecke, Hendrik Sándor, Bulcsú Gros, Claudius How to test for partially predictable chaos |
title | How to test for partially predictable chaos |
title_full | How to test for partially predictable chaos |
title_fullStr | How to test for partially predictable chaos |
title_full_unstemmed | How to test for partially predictable chaos |
title_short | How to test for partially predictable chaos |
title_sort | how to test for partially predictable chaos |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5430683/ https://www.ncbi.nlm.nih.gov/pubmed/28439074 http://dx.doi.org/10.1038/s41598-017-01083-x |
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