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فهرست نشریات دارای اعتبار وزارت علوم، تحقیقات و فناوری
پایگاه استنادی علوم جهان اسلام (ISC)

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Dana Mazraeh, Hassan, Nosrati Firoozsalari, Ali, Afzal Aghaei, Alireza, Parand, Kourosh. (1404). A Novel Neural Network Architecture for Solving Fractional Differential Equations. سامانه مدیریت نشریات علمی, (), -. doi: 10.22034/cmde.2025.63059.2803
Hassan Dana Mazraeh; Ali Nosrati Firoozsalari; Alireza Afzal Aghaei; Kourosh Parand. "A Novel Neural Network Architecture for Solving Fractional Differential Equations". سامانه مدیریت نشریات علمی, , , 1404, -. doi: 10.22034/cmde.2025.63059.2803
Dana Mazraeh, Hassan, Nosrati Firoozsalari, Ali, Afzal Aghaei, Alireza, Parand, Kourosh. (1404). 'A Novel Neural Network Architecture for Solving Fractional Differential Equations', سامانه مدیریت نشریات علمی, (), pp. -. doi: 10.22034/cmde.2025.63059.2803
Dana Mazraeh, Hassan, Nosrati Firoozsalari, Ali, Afzal Aghaei, Alireza, Parand, Kourosh. A Novel Neural Network Architecture for Solving Fractional Differential Equations. سامانه مدیریت نشریات علمی, 1404; (): -. doi: 10.22034/cmde.2025.63059.2803

A Novel Neural Network Architecture for Solving Fractional Differential Equations

Computational Methods for Differential Equations
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 23 شهریور 1404    اصل مقاله (2.32 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22034/cmde.2025.63059.2803
نویسندگان
Hassan Dana Mazraeh1؛ Ali Nosrati Firoozsalari2؛ Alireza Afzal Aghaei2؛ Kourosh Parand* 2
1School of Mathematics and Computer Sciences, Damghan University, Damghan, P.O. Box 36715-364, Iran.
2Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran.
چکیده
The primary objective of this research is to develop a neural network-based method for solving fractional differential equations. The proposed design incorporates a Gaussian integration rule and an $L1$ discretization technique for solving fractional (integro-) differential equations. In each equation, a multi-layer neural network is employed to approximate the unknown function. To demonstrate the versatility of the method, three forms of fractional differential equations are examined: a fractional ordinary differential equation, a fractional integro-differential equation, and a fractional partial differential equation. The results indicate that the proposed architecture demonstrates good accuracy for these different types of equations.
کلیدواژه‌ها
Artificial Neural Networks؛ Machine-Learning؛ Partial-Differential Equations؛ Fractional Calculus
آمار
تعداد مشاهده مقاله: 5
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