Fractional dynamics in complex media: a meshless numerical framework for distributed-order models with Riesz diffusion.

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Title: Fractional dynamics in complex media: a meshless numerical framework for distributed-order models with Riesz diffusion.
Authors: Derakhshan, Mohammad Hossein1 (AUTHOR) M.h.derakhshan.20@gmail.com, Irandoust Pakchin, Safar1 (AUTHOR)
Source: Soft Computing - A Fusion of Foundations, Methodologies & Applications. Jun2026, Vol. 30 Issue 6, p3929-3950. 22p.
Subjects: Caputo fractional derivatives, Meshfree methods, Numerical analysis, Fractional calculus, Fick's laws of diffusion, Operator theory, Fractional differential equations
Abstract: This work investigates a three-dimensional coupled system of distributed-order fractional differential equations involving Caputo-type time derivatives and Riesz fractional spatial operators. The model is designed to capture complex physical phenomena characterized by memory effects and spatial nonlocality, which commonly arise in applications such as porous media flow, viscoelastic materials, and anomalous diffusion processes. To efficiently solve this system, we develop a fully discrete numerical scheme that integrates a high-order temporal discretization based on quadrature approximations of the distributed-order Caputo derivatives with a meshless collocation method for spatial discretization. The proposed approach offers flexibility in handling irregular geometries without the need for structured grids. Rigorous convergence and stability analyses are presented, yielding optimal error estimates under suitable regularity assumptions. Numerical experiments are performed to validate the accuracy and efficiency of the method, including tests with both exact solutions and more realistic scenarios lacking analytical solutions. The results demonstrate that the proposed scheme is accurate, stable, and computationally efficient, making it well suited for practical applications involving high-dimensional, nonlocal fractional systems. [ABSTRACT FROM AUTHOR]
Copyright of Soft Computing - A Fusion of Foundations, Methodologies & 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: <searchLink fieldCode="DE" term="%22Caputo+fractional+derivatives%22">Caputo fractional derivatives</searchLink><br /><searchLink fieldCode="DE" term="%22Meshfree+methods%22">Meshfree methods</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+analysis%22">Numerical analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Fractional+calculus%22">Fractional calculus</searchLink><br /><searchLink fieldCode="DE" term="%22Fick's+laws+of+diffusion%22">Fick's laws of diffusion</searchLink><br /><searchLink fieldCode="DE" term="%22Operator+theory%22">Operator theory</searchLink><br /><searchLink fieldCode="DE" term="%22Fractional+differential+equations%22">Fractional differential equations</searchLink>
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  Data: This work investigates a three-dimensional coupled system of distributed-order fractional differential equations involving Caputo-type time derivatives and Riesz fractional spatial operators. The model is designed to capture complex physical phenomena characterized by memory effects and spatial nonlocality, which commonly arise in applications such as porous media flow, viscoelastic materials, and anomalous diffusion processes. To efficiently solve this system, we develop a fully discrete numerical scheme that integrates a high-order temporal discretization based on quadrature approximations of the distributed-order Caputo derivatives with a meshless collocation method for spatial discretization. The proposed approach offers flexibility in handling irregular geometries without the need for structured grids. Rigorous convergence and stability analyses are presented, yielding optimal error estimates under suitable regularity assumptions. Numerical experiments are performed to validate the accuracy and efficiency of the method, including tests with both exact solutions and more realistic scenarios lacking analytical solutions. The results demonstrate that the proposed scheme is accurate, stable, and computationally efficient, making it well suited for practical applications involving high-dimensional, nonlocal fractional systems. [ABSTRACT FROM AUTHOR]
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  Label:
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  Data: <i>Copyright of Soft Computing - A Fusion of Foundations, Methodologies & 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/s00500-025-11062-4
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      – Code: eng
        Text: English
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      – SubjectFull: Caputo fractional derivatives
        Type: general
      – SubjectFull: Meshfree methods
        Type: general
      – SubjectFull: Numerical analysis
        Type: general
      – SubjectFull: Fractional calculus
        Type: general
      – SubjectFull: Fick's laws of diffusion
        Type: general
      – SubjectFull: Operator theory
        Type: general
      – SubjectFull: Fractional differential equations
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      – TitleFull: Fractional dynamics in complex media: a meshless numerical framework for distributed-order models with Riesz diffusion.
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              M: 06
              Text: Jun2026
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              Y: 2026
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