Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

 
 
DownloadE-Mail
  A few-shot identification method for stochastic dynamical systems based on residual multipeaks adaptive sampling

An, X.-K., Du, L., Jiang, F., Zhang, Y.-J., Deng, Z.-C., Kurths, J. (2024): A few-shot identification method for stochastic dynamical systems based on residual multipeaks adaptive sampling. - Chaos, 34, 7, 073118.
https://doi.org/10.1063/5.0209779

Item is

Dateien

einblenden: Dateien
ausblenden: Dateien
:
an_2024_073118_1_5.0209779.pdf (Verlagsversion), 5MB
 
Datei-Permalink:
-
Name:
an_2024_073118_1_5.0209779.pdf
Beschreibung:
-
Sichtbarkeit:
Privat (Embargo bis 2025-07-10)
MIME-Typ / Prüfsumme:
application/pdf
Technische Metadaten:
Copyright Datum:
-
Copyright Info:
-
Lizenz:
-

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
An, Xiao-Kai, Autor
Du, Lin, Autor
Jiang, Feng, Autor
Zhang, Yu-Jia, Autor
Deng, Zi-Chen, Autor
Kurths, Jürgen1, Autor              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: Neural networks are popular data-driven modeling tools that come with high data collection costs. This paper proposes a residual-based multipeaks adaptive sampling (RMAS) algorithm, which can reduce the demand for a large number of samples in the identification of stochastic dynamical systems. Compared to classical residual-based sampling algorithms, the RMAS algorithm achieves higher system identification accuracy without relying on any hyperparameters. Subsequently, combining the RMAS algorithm and neural network, a few-shot identification (FSI) method for stochastic dynamical systems is proposed, which is applied to the identification of a vegetation biomass change model and the Rayleigh–Van der Pol impact vibration model. We show that the RMAS algorithm modifies residual-based sampling algorithms and, in particular, reduces the system identification error by 76% with the same sample sizes. Moreover, the surrogate model accurately predicts the first escape probability density function and the P bifurcation behavior in the systems, with the error of less than 1.59 x 10-2⁠. Finally, the robustness of the FSI method is validated.

Details

einblenden:
ausblenden:
Sprache(n): eng - Englisch
 Datum: 2024-07-092024-07-09
 Publikationsstatus: Final veröffentlicht
 Seiten: 14
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1063/5.0209779
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Nonlinear Dynamics
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: Chaos
Genre der Quelle: Zeitschrift, SCI, Scopus, p3
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 34 (7) Artikelnummer: 073118 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/180808
Publisher: American Institute of Physics (AIP)