Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
Abstract
This paper introduces a new numerical solution based on the least squares support vector machine (LS-SVR) for solving nonlinear ordinary differential equations of high dimensionality. We apply the quasilinearization method to linearize the magnetohydrodynamic (MHD) flow of nanofluid around a stretching cylinder, thereby transforming it into a linear problem. We then utilize LS-SVR with fractional Hermite functions as basis functions to solve this problem over a semi-infinite interval. Our numerical results confirm the effectiveness of this approach.
Pakniyat,A . (2022). Numerical solution for solving magnetohydrodynamic (MHD) flow of nanofluid by least squares support vector regression. Computational Mathematics and Computer Modeling with Applications (CMCMA), 1(2), 104-121. doi: 10.52547/CMCMA.1.2.104
MLA
Pakniyat,A . "Numerical solution for solving magnetohydrodynamic (MHD) flow of nanofluid by least squares support vector regression", Computational Mathematics and Computer Modeling with Applications (CMCMA), 1, 2, 2022, 104-121. doi: 10.52547/CMCMA.1.2.104
HARVARD
Pakniyat A. (2022). 'Numerical solution for solving magnetohydrodynamic (MHD) flow of nanofluid by least squares support vector regression', Computational Mathematics and Computer Modeling with Applications (CMCMA), 1(2), pp. 104-121. doi: 10.52547/CMCMA.1.2.104
CHICAGO
A Pakniyat, "Numerical solution for solving magnetohydrodynamic (MHD) flow of nanofluid by least squares support vector regression," Computational Mathematics and Computer Modeling with Applications (CMCMA), 1 2 (2022): 104-121, doi: 10.52547/CMCMA.1.2.104
VANCOUVER
Pakniyat A. Numerical solution for solving magnetohydrodynamic (MHD) flow of nanofluid by least squares support vector regression. Computational Mathematics and Computer Modeling with Applications (CMCMA). 2022;1(2):104-121. doi: 10.52547/CMCMA.1.2.104
