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Surrogate Modeling Methodology for Nonlinear Atmospheric Dynamics: From Conceptual Model to Neural Networks.

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Title: Surrogate Modeling Methodology for Nonlinear Atmospheric Dynamics: From Conceptual Model to Neural Networks.
Authors: Soldatenko, S. A.1 (AUTHOR) soldatenko@aari.ru, Angudovich, Ya. I.1,2 (AUTHOR)
Source: Russian Meteorology & Hydrology. Feb2025, Vol. 50 Issue 2, p91-101. 11p.
Subject Terms: *Machine learning, *Long short-term memory, *Nonlinear dynamical systems, *Numerical weather forecasting, *Artificial intelligence
Abstract: The paper examines a methodological approach to simulating nonlinear atmospheric dynamics. The approach implies constructing a surrogate (replacement) model of a physical object (process) based on machine learning. For illustrative purposes, the surrogate model is built for the conceptual model of the coupled ocean–atmosphere system, in which the atmospheric component is represented by a low-dimensional nonlinear dynamic system, and the harmonic oscillator model is used as the oceanic component. The surrogate model is based on unidirectional and bidirectional long short-term memory neural networks (LSTM and BiLSTM, respectively). Nine LSTMs and one BiLSTM, whose structures were determined experimentally, are analyzed to evaluate their ability to predict complex dynamics and chaotic regime in the examined model on time intervals from 5 to 10 days. The best forecast accuracy was obtained using the BiLSTM. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 184977584
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Surrogate Modeling Methodology for Nonlinear Atmospheric Dynamics: From Conceptual Model to Neural Networks.
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  Data: <searchLink fieldCode="AR" term="%22Soldatenko%2C+S%2E+A%2E%22">Soldatenko, S. A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> soldatenko@aari.ru</i><br /><searchLink fieldCode="AR" term="%22Angudovich%2C+Ya%2E+I%2E%22">Angudovich, Ya. I.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Russian+Meteorology+%26+Hydrology%22">Russian Meteorology & Hydrology</searchLink>. Feb2025, Vol. 50 Issue 2, p91-101. 11p.
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  Data: *<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br />*<searchLink fieldCode="DE" term="%22Nonlinear+dynamical+systems%22">Nonlinear dynamical systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Numerical+weather+forecasting%22">Numerical weather forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The paper examines a methodological approach to simulating nonlinear atmospheric dynamics. The approach implies constructing a surrogate (replacement) model of a physical object (process) based on machine learning. For illustrative purposes, the surrogate model is built for the conceptual model of the coupled ocean–atmosphere system, in which the atmospheric component is represented by a low-dimensional nonlinear dynamic system, and the harmonic oscillator model is used as the oceanic component. The surrogate model is based on unidirectional and bidirectional long short-term memory neural networks (LSTM and BiLSTM, respectively). Nine LSTMs and one BiLSTM, whose structures were determined experimentally, are analyzed to evaluate their ability to predict complex dynamics and chaotic regime in the examined model on time intervals from 5 to 10 days. The best forecast accuracy was obtained using the BiLSTM. [ABSTRACT FROM AUTHOR]
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        Value: 10.3103/S1068373925020013
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      – Code: eng
        Text: English
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        PageCount: 11
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      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Long short-term memory
        Type: general
      – SubjectFull: Nonlinear dynamical systems
        Type: general
      – SubjectFull: Numerical weather forecasting
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
    Titles:
      – TitleFull: Surrogate Modeling Methodology for Nonlinear Atmospheric Dynamics: From Conceptual Model to Neural Networks.
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            NameFull: Soldatenko, S. A.
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            NameFull: Angudovich, Ya. I.
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              Text: Feb2025
              Type: published
              Y: 2025
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