• Journal of applied mathematics & informatics
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• 제26권1_2호
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• pp.135-149
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• 2008

Many deterministic systems are described by Ordinary differential equations and can often be improved by including stochastic effects, but numerical methods for solving stochastic differential equations(SDEs) are required, and work in this area is far less advanced than for deterministic differential equations. In this paper,first we follow [7] to describe Runge-Kutta methods with order 2 from Taylor approximations in the weak sense and present two well known Runge-Kutta methods, RK2-TO and RK2-PL. Then we obtain a new 3-stage explicit Runge-Kutta with order 2 in weak sense and compare the numerical results among these three methods.

• 대한수학회논문집
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• 제26권4호
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• pp.709-720
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• 2011

We obtain special type of differential equations which their solution are random variable with known continuous density function. Stochastic differential equations (SDE) of continuous distributions are determined by the Fokker-Planck theorem. We approximate solution of differential equation with numerical methods such as: the Euler-Maruyama and ten stages explicit Runge-Kutta method, and analysis error prediction statistically. Numerical results, show the performance of the Rung-Kutta method with respect to the Euler-Maruyama. The exponential two parameters, exponential, normal, uniform, beta, gamma and Parreto distributions are considered in this paper.