2021/02/12 by M. Sinhuber, Michael Sinhuber, J. Friedrich +5 · 12 citations
Engineering · Environmental Science · Mathematics · Physics and Astronomy · #Algorithm #Computer science #Ecosystem dynamics and resilience #Fluid Dynamics and Turbulent Flows #Mathematics #Nonlinear system #Physics #Plant Water Relations and Carbon Dynamics #Probability and statistics #Probability density function #Range (aeronautics) #Reynolds number #Scale (ratio) #Series (stratigraphy) #Statistical physics #Statistics #Turbulence #physics.data-an #physics.flu-dyn
paper · pdf · open access · doi:10.1088/1367-2630/abe60e
published in New Journal of Physics 23(6), 063063 (IOP Publishing) · 15 pages, seven figures
openalex publication_date 2021/02/12 · arxiv created 2021/02/16 · openalex created_date 2021/03/01 · arxiv updated 2021/08/11 · openalex updated_date 2026/08/05
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.