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: | M Sinhuber, J Friedrich, R Grauer, M Wilczek |
---|---|
Format: | Article |
Language: | English |
Published: |
IOP Publishing
2021-01-01
|
Series: | New Journal of Physics |
Subjects: | |
Online Access: | https://doi.org/10.1088/1367-2630/abe60e |
Similar Items
-
Generalized Description of Intermittency in Turbulence via Stochastic Methods
by: Jan Friedrich, et al.
Published: (2020-09-01) -
Focus on stochastic flows and climate statistics
by: JB Marston, et al.
Published: (2016-01-01) -
The Influence of Initial Purity Level on the Refining Efficiency of Aluminum via Zone Refining
by: Xiaoxin Zhang, et al.
Published: (2021-01-01) -
Role of stochastic scattering in Virgo A turbulent regions
by: M. V. Sydorenko, et al.
Published: (2013-12-01) -
Generative Data‐Driven Approaches for Stochastic Subgrid Parameterizations in an Idealized Ocean Model
by: Pavel Perezhogin, et al.
Published: (2023-10-01)