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  Quantifying model uncertainty for the observed non-Gaussian data by the Hellinger distance

Zheng, Y., Yang, F., Duan, J., Kurths, J. (2021): Quantifying model uncertainty for the observed non-Gaussian data by the Hellinger distance. - Communications in Nonlinear Science and Numerical Simulation, 96, 105720.
https://doi.org/10.1016/j.cnsns.2021.105720

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 Creators:
Zheng, Yayun1, Author
Yang, Fang1, Author
Duan, Jinqiao1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Mathematical models for complex systems under random fluctuations often certain uncertain parameters. However, quantifying model uncertainty for a stochastic differential equation with an -stable Lévy process is still lacking. Here, we propose an approach to infer all the uncertain non-Gaussian parameters and other system parameters by minimizing the Hellinger distance over the parameter space. The Hellinger distance measures the similarity between an empirical probability density of non-Gaussian observations and a solution (as a probability density) of the associated nonlocal Fokker-Planck equation. Numerical experiments verify that our method is feasible for estimating single and multiple parameters. Meanwhile, we find an optimal estimation interval of the estimated parameters. This method is beneficial for extracting governing dynamical system models under non-Gaussian fluctuations, as in the study of abrupt climate changes in the Dansgaard-Oeschger events.

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 Dates: 2021-01-142021-05-01
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.cnsns.2021.105720
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Nonlinear Data Analysis
 Degree: -

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Title: Communications in Nonlinear Science and Numerical Simulation
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 96 Sequence Number: 105720 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/201610061
Publisher: Elsevier