Multi-level stochastic refinement for complex time series and fields: a data-driven approach
Spatio-temporally extended nonlinear systems often exhibit a remarkable complexity in space and time. In many cases, extensive datasets of such systems are difficult to obtain, yet needed for a range of applications. Here, we present a method to generate synthetic time series or fields that reproduc...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2021-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/abe60e |
| _version_ | 1797750205813096448 |
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| author | M Sinhuber J Friedrich R Grauer M Wilczek |
| author_facet | M Sinhuber J Friedrich R Grauer M Wilczek |
| author_sort | M Sinhuber |
| collection | DOAJ |
| description | Spatio-temporally extended nonlinear systems often exhibit a remarkable complexity in space and time. In many cases, extensive datasets of such systems are difficult to obtain, yet needed for a range of applications. Here, we present a method to generate synthetic time series or fields that reproduce statistical multi-scale features of complex systems. The method is based on a hierarchical refinement employing transition probability density functions (PDFs) from one scale to another. We address the case in which such PDFs can be obtained from experimental measurements or simulations and then used to generate arbitrarily large synthetic datasets. The validity of our approach is demonstrated at the example of an experimental dataset of high Reynolds number turbulence. |
| first_indexed | 2024-03-12T16:30:22Z |
| format | Article |
| id | doaj.art-494204ef473047b28a21c5c3a82a9a52 |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:30:22Z |
| publishDate | 2021-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-494204ef473047b28a21c5c3a82a9a522023-08-08T15:32:31ZengIOP PublishingNew Journal of Physics1367-26302021-01-0123606306310.1088/1367-2630/abe60eMulti-level stochastic refinement for complex time series and fields: a data-driven approachM Sinhuber0https://orcid.org/0000-0002-6013-8111J Friedrich1https://orcid.org/0000-0002-9862-6268R Grauer2https://orcid.org/0000-0003-0622-071XM Wilczek3https://orcid.org/0000-0002-1423-8285Max Planck Institute for Dynamics and Self-Organization , Am Faßberg 17, D-37077 Göttingen, GermanyTheoretische Physik I, Ruhr-Universität Bochum , Universitätsstr. 150, D-44780 Bochum, Germany; Univ. Lyon, ENS de Lyon, Univ. Claude Bernard , CNRS, Laboratoire de Physique, F-69342, Lyon, FranceTheoretische Physik I, Ruhr-Universität Bochum , Universitätsstr. 150, D-44780 Bochum, GermanyMax Planck Institute for Dynamics and Self-Organization , Am Faßberg 17, D-37077 Göttingen, GermanySpatio-temporally extended nonlinear systems often exhibit a remarkable complexity in space and time. In many cases, extensive datasets of such systems are difficult to obtain, yet needed for a range of applications. Here, we present a method to generate synthetic time series or fields that reproduce statistical multi-scale features of complex systems. The method is based on a hierarchical refinement employing transition probability density functions (PDFs) from one scale to another. We address the case in which such PDFs can be obtained from experimental measurements or simulations and then used to generate arbitrarily large synthetic datasets. The validity of our approach is demonstrated at the example of an experimental dataset of high Reynolds number turbulence.https://doi.org/10.1088/1367-2630/abe60esynthetic datastochastic refinementturbulence models |
| spellingShingle | M Sinhuber J Friedrich R Grauer M Wilczek Multi-level stochastic refinement for complex time series and fields: a data-driven approach New Journal of Physics synthetic data stochastic refinement turbulence models |
| title | Multi-level stochastic refinement for complex time series and fields: a data-driven approach |
| title_full | Multi-level stochastic refinement for complex time series and fields: a data-driven approach |
| title_fullStr | Multi-level stochastic refinement for complex time series and fields: a data-driven approach |
| title_full_unstemmed | Multi-level stochastic refinement for complex time series and fields: a data-driven approach |
| title_short | Multi-level stochastic refinement for complex time series and fields: a data-driven approach |
| title_sort | multi level stochastic refinement for complex time series and fields a data driven approach |
| topic | synthetic data stochastic refinement turbulence models |
| url | https://doi.org/10.1088/1367-2630/abe60e |
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