deepFDEnet: A Novel Neural Network Architecture for Solving Fractional Differential Equations

Autor: Firoozsalari, Ali Nosrati, Mazraeh, Hassan Dana, Aghaei, Alireza Afzal, Parand, Kourosh

The primary goal of this research is to propose a novel architecture for a deep neural network that can solve fractional differential equations accurately. A Gaussian integration rule and a $L_1$ discretization technique are used in the proposed desi

Externí odkaz: http://arxiv.org/abs/2309.07684

A NEW METHOD BASED ON LEAST-SQUARES SUPPORT VECTOR REGRESSION FOR SOLVING OPTIMAL CONTROL PROBLEMS.

Autor: BOLHASSANI, MITRA, MAZRAEH, HASSAN DANA, PARAND, KOUROSH

Publikováno v: Kybernetika; 2024, Vol. 60 Issue 4, p513-534, 22p

COMBINING GENETIC ALGORITHM AND SINC-GALERKIN METHOD FOR SOLVING AN INVERSE DIFFUSION PROBLEM

Autor: MAZRAEH, Hassan Dana, POURGHOLİ, R., T.HOULARİ,

Publikováno v: TWMS Journal of Applied and Engineering Mathematics, Vol 7, Iss 1, Pp 33-50 (2017)
Volume: 7, Issue: 1 33-50
TWMS Journal of Applied and Engineering Mathematics

A numerical approach combining the use of a genetic algorithm with the solution of the Sinc-Galerkin method is proposed for the determination of an unknown time-dependent diffusivity a(t) in an inverse diffusion problem (IDP). At the beginning of the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::93b29c8bc1522e4278b24384d0ab6288
http://jaem.isikun.edu.tr/web/index.php/current/93-vol7no1/276-combining-genetic-algorithm-and-sinc-galerkin-method-for-solving-an-inverse-diffusion-problem

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The fully-implicit finite difference method for solving nonlinear inverse parabolic problems with unknown source term

Autor: Mazraeh, Hassan Dana, Pourgholi, Reza, Tavana, Sahar

Publikováno v: International Journal of Computing Science and Mathematics; 2018, Vol. 9 Issue: 4 p405-418, 14p