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  Multi-level stochastic refinement for complex time series and fields: a data-driven approach

Sinhuber, M., Friedrich, J., Grauer, R., & Wilczek, M. (2021). Multi-level stochastic refinement for complex time series and fields: a data-driven approach. New Journal of Physics, 23: 063063. doi:10.1088/1367-2630/abe60e.

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 Creators:
Sinhuber, Michael1, Author           
Friedrich, Jan, Author
Grauer, Rainer, Author
Wilczek, Michael1, Author           
Affiliations:
1Max Planck Research Group Theory of Turbulent Flows, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2266693              

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 Abstract: 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.

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Language(s): eng - English
 Dates: 2021-06-252021
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1088/1367-2630/abe60e
 Degree: -

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Title: New Journal of Physics
  Abbreviation : New J. Phys.
Source Genre: Journal
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Publ. Info: Bristol : IOP Publishing
Pages: 10 Volume / Issue: 23 Sequence Number: 063063 Start / End Page: - Identifier: ISSN: 1367-2630
CoNE: https://pure.mpg.de/cone/journals/resource/954926913666