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  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

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???ViewItemFull_lblCiteItemAs???: https://publications.pik-potsdam.de/pubman/item/item_30728 ???ViewItemFull_lblCiteItemVersionAs???: https://publications.pik-potsdam.de/pubman/item/item_30728_2
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an_2024_073118_1_5.0209779.pdf
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 ???ViewItemFull_lblCreators???:
An, Xiao-Kai, ???ENUM_CREATORROLE_AUTHOR???
Du, Lin, ???ENUM_CREATORROLE_AUTHOR???
Jiang, Feng, ???ENUM_CREATORROLE_AUTHOR???
Zhang, Yu-Jia, ???ENUM_CREATORROLE_AUTHOR???
Deng, Zi-Chen, ???ENUM_CREATORROLE_AUTHOR???
Kurths, Jürgen1, ???ENUM_CREATORROLE_AUTHOR???           
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1Potsdam Institute for Climate Impact Research, ou_persistent13              

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 ???ViewItemFull_lblAbstract???: 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.

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???ViewItemFull_lblLanguages???: eng - English
 ???ViewItemFull_lblDates???: 2024-07-092024-07-09
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 ???ViewItemFull_lblIdentifiers???: ???ENUM_IDENTIFIERTYPE_DOI???: 10.1063/5.0209779
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???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: RD4 - Complexity Science
???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: RD4 - Complexity Science
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Nonlinear Dynamics
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???ViewItemFull_lblSourceTitle???: Chaos
???ViewItemFull_lblSourceGenre???: ???ENUM_GENRE_JOURNAL???, SCI, Scopus, p3
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???ViewItemFull_lblPages???: ???lbl_noEntry??? ???ViewItemFull_lblSourceVolumeIssue???: 34 (7) ???ViewItemFull_lblSourceSequenceNo???: 073118 ???ViewItemFull_lblSourceStartEndPage???: ???lbl_noEntry??? ???ViewItemFull_lblSourceIdentifier???: ???ENUM_IDENTIFIERTYPE_CONE???: https://publications.pik-potsdam.de/cone/journals/resource/180808
???ENUM_IDENTIFIERTYPE_PUBLISHER???: American Institute of Physics (AIP)