Journal of Mathematics and Statistics

Advanced Numerical Methods for Differential Equations in Applied Sciences

Description

The literature reveals that numerous real-life phenomena in applied sciences (such as physics, engineering, finance, medicine, etc.) can be modelled by highly nonlinear Differential Equations (DEs) or by  Stochastic Differential Equations (SDEs) with unknown analytical solutions. Therefore, the numerical solutions for such (DEs) and (SDEs) have received a huge attention from mathematicians, physicists, and engineers for the sake of approximating their analytical solutions.  

The main target of this special issue is to create a multidisciplinary forum of discussions on the most recent results in this field of research. More precisely, we will focus on recent advanced numerical studies on Differential Equations and Stochastic Differential Equations related to applied sciences. In addition, the well-developed analysis of existing numerical algorithms in terms of efficiency, applicability, convergence, stability and accuracy is of importance. A discussion of nontrivial numerical examples is encouraged.

Topics of interest include, but are not limited to:

1. Numerical methods for Ordinary differential equations.
2. Numerical methods for Partial differential equations.
3. Numerical methods for Stochastic differential equations.
4. Numerical methods for Fractional differential equations.
5. Numerical methods for Fractional difference equations.
6. Numerical methods for Fractional integro-differential equations.
7. Numerical methods for fuzzy initial and boundary value problems.
8. Mathematical control theory. Stochastic modelling.

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