The main idea of this paper is to demonstrate a stochastic computational technique consisting of the generalized stochastic perturbation method using the Taylor expansions of random variables and the classical Finite Difference Method based on regular grids. As it is documented by computational illustrations, it is possible to determine, using this approach, also higher probabilistic moments for any random dispersion of input variables unlike in the second order second moment technique worked out before. A numerical algorithm is implemented here using straightforward partial differentiation of hierarchical equations with respect to the random input quantity and further symbolic computations of probabilistic moments and characteristics by th...
A new efficient technique to impose the statistical correlation when using Monte Carlo type method f...
AbstractOne method of approaching models represented by systems of stochastic ordinary differential ...
The development of numerical methods for stochastic differential equations has intensified over the ...
The main idea of this paper is to demonstrate a stochastic computational technique consisting of the...
The main idea here is to demonstrate the new stochastic discrete computational approach consisting o...
The main aim is to present recent developments in applications of symbolic computing in probabilisti...
The main aim of this work is to contrast three various probabilistic computational techniques, namel...
In the paper an efficient algorithm is formulated to eliminate inherent secular effects in solving e...
The main idea of this work is to demonstrate an application of the generalized perturbation-based St...
The application of the stochastic perturbation technique in the classical Hertz contact problem of a...
In the article, the linear dynamic random model of n–th order described by the random state equation...
This work develops numerical techniques for the simulation of systems with stochastic parameters, mo...
We introduce Sim.DiffProc, an R package for symbolic and numerical computations on scalar and multiv...
The focus of this paper is to develop efficient numerical schemes for analysis of systems governed b...
Using a computational method to solve parabolic differential equations is a feat that can be done wi...
A new efficient technique to impose the statistical correlation when using Monte Carlo type method f...
AbstractOne method of approaching models represented by systems of stochastic ordinary differential ...
The development of numerical methods for stochastic differential equations has intensified over the ...
The main idea of this paper is to demonstrate a stochastic computational technique consisting of the...
The main idea here is to demonstrate the new stochastic discrete computational approach consisting o...
The main aim is to present recent developments in applications of symbolic computing in probabilisti...
The main aim of this work is to contrast three various probabilistic computational techniques, namel...
In the paper an efficient algorithm is formulated to eliminate inherent secular effects in solving e...
The main idea of this work is to demonstrate an application of the generalized perturbation-based St...
The application of the stochastic perturbation technique in the classical Hertz contact problem of a...
In the article, the linear dynamic random model of n–th order described by the random state equation...
This work develops numerical techniques for the simulation of systems with stochastic parameters, mo...
We introduce Sim.DiffProc, an R package for symbolic and numerical computations on scalar and multiv...
The focus of this paper is to develop efficient numerical schemes for analysis of systems governed b...
Using a computational method to solve parabolic differential equations is a feat that can be done wi...
A new efficient technique to impose the statistical correlation when using Monte Carlo type method f...
AbstractOne method of approaching models represented by systems of stochastic ordinary differential ...
The development of numerical methods for stochastic differential equations has intensified over the ...