Abstract:This paper focuses on an efficient spline-based numerical technique for numerically addressing a second-order Volterra partial integrodifferential equation. The time derivative is discretized using a finite difference scheme, while the space derivative is approximated using the extended cubic
B
-spline basis. The scheme is also tested for stability study to ensure that the errors do not accumulate. The convergence of the pro… Show more
“…The BBME has been solved numerically by various methods based on finite difference, finite element, Petrov-Galerkin finite element, Fourier pseudo-spectral, collocation methods, and many other methods [4][5]. Lately, the B-spline collocation method with quadratic B-spline [4], cubic B-spline (CB) [6][7][8], cubic trigonometric B-spline (CTB) [9][10][11], and extended cubic B-spline (ECB) [12][13][14][15][16] have been formulated successfully on some differential equations with accurate results and high efficiency. There are several techniques used to handle the nonlinear term in the BBME namely Taylor series expansion [17], quasilinearization [18] and Adomian polynomials [19].…”
Extended cubic B-spline collocation method is formulated to solve the Benjamin-Bona-Mahony equation without linearization. The Besse relaxation scheme is applied on the nonlinear terms and therefore transforms the equation into a systemof two linear equations. The time derivative is discretized using Forward Difference Approximation whereas the spatial dimension is approximated using extended cubic B-spline function. Applying the von-Neumann stability analysis, the proposed technique are shown unconditionally stable. Two numerical examples are presented and the results are compared with the exact solutions and recent methods.
“…The BBME has been solved numerically by various methods based on finite difference, finite element, Petrov-Galerkin finite element, Fourier pseudo-spectral, collocation methods, and many other methods [4][5]. Lately, the B-spline collocation method with quadratic B-spline [4], cubic B-spline (CB) [6][7][8], cubic trigonometric B-spline (CTB) [9][10][11], and extended cubic B-spline (ECB) [12][13][14][15][16] have been formulated successfully on some differential equations with accurate results and high efficiency. There are several techniques used to handle the nonlinear term in the BBME namely Taylor series expansion [17], quasilinearization [18] and Adomian polynomials [19].…”
Extended cubic B-spline collocation method is formulated to solve the Benjamin-Bona-Mahony equation without linearization. The Besse relaxation scheme is applied on the nonlinear terms and therefore transforms the equation into a systemof two linear equations. The time derivative is discretized using Forward Difference Approximation whereas the spatial dimension is approximated using extended cubic B-spline function. Applying the von-Neumann stability analysis, the proposed technique are shown unconditionally stable. Two numerical examples are presented and the results are compared with the exact solutions and recent methods.
The application of the local polynomial splines to the solution of integro-differential equations was regarded in the author’s previous papers. In a recent paper, we introduced the application of the local nonpolynomial splines to the solution of integro-differential equations. These splines allow us to approximate functions with a presribed order of approximation. In this paper, we apply the splines to the solution of the integro-differential equations with a smooth kernel. Applying the trigonometric or exponential spline approximations of the fifth order of approximation, we obtain an approximate solution of the integro-differential equation at the set of nodes. The advantages of using such splines include the ability to determine not only the values of the desired function at the grid nodes, but also the first derivative at the grid nodes. The obtained values can be connected by lines using the splines. Thus, after interpolation, we can obtain the value of the solution at any point of the considered interval. Several numerical examples are given.
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