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

Title data

Sinhuber, Michael ; Friedrich, Jan ; Grauer, Rainer ; Wilczek, Michael:
Multi-level stochastic refinement for complex time series and fields : a data-driven approach.
In: New Journal of Physics. Vol. 23 (2021) Issue 6 . - No. 063063.
ISSN 1367-2630
DOI: https://doi.org/10.1088/1367-2630/abe60e

Abstract in another language

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.

Further data

Item Type: Article in a journal
Refereed: Yes
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Physics > Chair Theoretical Physics I > Chair Theoretical Physics I - Univ.-Prof. Dr. Michael Wilczek
Profile Fields > Advanced Fields > Nonlinear Dynamics
Result of work at the UBT: No
DDC Subjects: 500 Science > 530 Physics
Date Deposited: 24 Feb 2022 07:24
Last Modified: 24 Feb 2022 07:24
URI: https://eref.uni-bayreuth.de/id/eprint/67605