Radial Kernels Collocation Method for the Solution of Volterra Integro-Differential Equations

Radial kernel interpolation is an advanced method in approximation theory for the construction of higher order accurate interpolants for scattered data up to higher dimensional spaces. In this manuscript, we formulate a radial kernel collocation approach for solving problems involving the Volterra integro-differential equations using two radial kernels: The Generalized Multi-quadrics and the linear Laguerre-Gaussians. This was achieved by simplifying the Volterra integral problem's solution to an algebraic system of equations. The impact of the shape parameter present in every kernel on the method's accuracy is examined and found to be significant. Two examples were used to illustrate the process; the numerical results are shown as tables and graphs. MATLAB 2018a was employed in the process.

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Publication Details

Journal
Science Engineering and Technology
Published
2024-10-11
DOI
https://doi.org/10.54327/set2024/v4.i2.160
Primary Topic
Numerical methods in inverse problems
Type
article
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Radial Kernels Collocation Method for the Solution of Volterra Integro-Differential Equations

Stephen Mkegh Nengem, Friday Haruna, Shalom Danjuma Bitrus
Science Engineering and Technology
Numerical methods in inverse problems
article

Radial Kernels Collocation Method for the Solution of Volterra Integro-Differential Equations

Stephen Mkegh Nengem, Friday Haruna, Shalom Danjuma Bitrus
article en

Abstract

Radial kernel interpolation is an advanced method in approximation theory for the construction of higher order accurate interpolants for scattered data up to higher dimensional spaces. In this manuscript, we formulate a radial kernel collocation approach for solving problems involving the Volterra integro-differential equations using two radial kernels: The Generalized Multi-quadrics and the linear Laguerre-Gaussians. This was achieved by simplifying the Volterra integral problem's solution to an algebraic system of equations. The impact of the shape parameter present in every kernel on the method's accuracy is examined and found to be significant. Two examples were used to illustrate the process; the numerical results are shown as tables and graphs. MATLAB 2018a was employed in the process.

Science Engineering and TechnologyVol. 4(2)
Taraba State University (NG)
Openalex Percentile: Top 70%
Numerical methods in inverse problems
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