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Modeling on magnetohydrodynamic Stokes flow using machine learning and curve fitting.

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Title: Modeling on magnetohydrodynamic Stokes flow using machine learning and curve fitting.
Authors: Gurbuz-Caldag, Merve1 (AUTHOR) merve.gurbuz@tedu.edu.tr, Pekmen, Bengisen1 (AUTHOR) bengisen.pekmen@tedu.edu.tr
Source: Neural Computing & Applications. Jun2025, Vol. 37 Issue 16, p9603-9619. 17p.
Subjects: Stokes flow, Stream function, Learning curve, Fluid flow, Numerical calculations
Abstract: In this study, neural network (NN) and curve fitting modeling of fluid flow characteristics of the magnetohydrodynamic (MHD) Stokes flow in a lid-driven cavity are utilized. Firstly, the MHD Stokes flow equations are numerically solved by the method of approximate particular solution for the variations of Hartmann number M ∈ [ 1 , 120 ] and the inclination angle a ∈ [ 0 , π ] . The essential data for modeling are extracted from the numerical results. The inputs are M and a, and the outputs are the infinity norm of stream function ψ , v velocity component, vorticity ω and the minimum value of u velocity. In modeling of these outputs, the distinct curve fitting functions are examined. NN is employed for different layer numbers and data partitions. It is obtained that the increase in the number of the hidden layers gives less error and locally weighted quadratic regression fit captures the best behavior in curve fitting. The usage of modeling allows us to be independent from the repeated numerical calculations. The capability of trilayer NN for modeling ψ , u , v , ω in the entire region is also shown. [ABSTRACT FROM AUTHOR]
Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Modeling on magnetohydrodynamic Stokes flow using machine learning and curve fitting.
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  Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Jun2025, Vol. 37 Issue 16, p9603-9619. 17p.
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  Data: <searchLink fieldCode="DE" term="%22Stokes+flow%22">Stokes flow</searchLink><br /><searchLink fieldCode="DE" term="%22Stream+function%22">Stream function</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+curve%22">Learning curve</searchLink><br /><searchLink fieldCode="DE" term="%22Fluid+flow%22">Fluid flow</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+calculations%22">Numerical calculations</searchLink>
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  Data: In this study, neural network (NN) and curve fitting modeling of fluid flow characteristics of the magnetohydrodynamic (MHD) Stokes flow in a lid-driven cavity are utilized. Firstly, the MHD Stokes flow equations are numerically solved by the method of approximate particular solution for the variations of Hartmann number M ∈ [ 1 , 120 ] and the inclination angle a ∈ [ 0 , π ] . The essential data for modeling are extracted from the numerical results. The inputs are M and a, and the outputs are the infinity norm of stream function ψ , v velocity component, vorticity ω and the minimum value of u velocity. In modeling of these outputs, the distinct curve fitting functions are examined. NN is employed for different layer numbers and data partitions. It is obtained that the increase in the number of the hidden layers gives less error and locally weighted quadratic regression fit captures the best behavior in curve fitting. The usage of modeling allows us to be independent from the repeated numerical calculations. The capability of trilayer NN for modeling ψ , u , v , ω in the entire region is also shown. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00521-025-11088-7
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      – Code: eng
        Text: English
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        PageCount: 17
        StartPage: 9603
    Subjects:
      – SubjectFull: Stokes flow
        Type: general
      – SubjectFull: Stream function
        Type: general
      – SubjectFull: Learning curve
        Type: general
      – SubjectFull: Fluid flow
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      – SubjectFull: Numerical calculations
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              M: 06
              Text: Jun2025
              Type: published
              Y: 2025
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