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  First-passage problem for stochastic differential equations with combined parametric Gaussian and Lévy white noises via path integral method

Zan, W., Xu, Y., Metzler, R., Kurths, J. (2021): First-passage problem for stochastic differential equations with combined parametric Gaussian and Lévy white noises via path integral method. - Journal of Computational Physics, 435, 110264.
https://doi.org/10.1016/j.jcp.2021.110264

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 Urheber:
Zan, Wanrong1, Autor
Xu, Yong1, Autor
Metzler, Ralf1, Autor
Kurths, Jürgen2, Autor              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: We study the first-passage problem for a process governed by a stochastic differential equation (SDE) driven simultaneously by both parametric Gaussian and Lévy white noises. We extend the path integral (PI) method to solve the SDE with this combined noise input and the corresponding fractional Fokker-Planck-Kolmogorov equations. Then, the PI solutions are modified to analyze the first-passage problem. Finally, numerical examples based on Monte Carlo simulations verify the extension of the PI method and the modification of the PI solutions. The detailed effects of the system parameters on the first-passage problem are analyzed.

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 Datum: 2021-03-042021-03-04
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1016/j.jcp.2021.110264
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Tipping Elements
Organisational keyword: RD4 - Complexity Science
 Art des Abschluß: -

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Titel: Journal of Computational Physics
Genre der Quelle: Zeitschrift, SCI, Scopus
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Seiten: - Band / Heft: 435 Artikelnummer: 110264 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journal-computational-physics
Publisher: Elsevier