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Issues in the software implementation of stochastic numerical Runge–Kutta
Gevorkyan M.N., Demidova A.V., Korolkova A.V., Kulyabov D.S.
This paper discusses the application of stochastic Runge-Kutta-like numerical methods with weak
Implementation difficulties analysis of stochastic numerical rungekutta methods
Kulyabov D.S., Gevorkyan M.N., Demidova A.V., Korolkova A.V., Sevastianov L.A., Kotukov M.M.
This article discusses stochastic numerical methods of Runge-Kutta methods with weak and strong
Automatic code generation for stochastic Runge-Kutta methods
Gevorkyan M.N., Demidova A.V., Korolkova A.V., Kulyabov D.S.
In this paper we consider in detail the realization of Runge-Kutta stochastic numerical methods
Implementation Difficulties Analysis of Stochastic Numerical Runge-Kutta Methods
Gevorkyan M.N., Demidova A.V., Korolkova A.V., Kulyabov D.S., Gostev I.M.
This article discusses stochastic numerical methods of Runge-Kutta type with weak and strong
On sharp bounds in the t. lyons's neo-classical inequality, which is essential for the theory of stochastic differential equations
Shishkina, E., Sitnik, S., Polunin, V.
In the famous paper of Terry Lyons a new approach to solving a general class of stochastic
Stochastic runge-kutta software package for stochastic differential equations
Gevorkyan M.N., Velieva T.R., Korolkova A.V., Kulyabov D.S., Sevastyanov L.A.
to apply stochastic Runge-Kutta methods for numerical study of stochastic differential equations
Computer research of the controlled models with migration flows
Demidova A.V., Druzhinina O.V., Masina O.N., Petrov A.A.
of numerical optimization methods in combination with methods for generating control functions. The transition
Implementation of an analytical-numerical approach to stochastization of one-step processes in the Julia programming language
Fedorov A.V., Masolova A.O., Korolkova A.V., Kulyabov D.S.
In this paper, we use the method of stochastization of one-step processes to demonstrate
Synthesis and Computer Study of Population Dynamics Controlled Models Using Methods of Numerical Optimization, Stochastization and Machine Learning
Demidova Anastasia V., Druzhinina Olga V., Masina Olga N., Petrov Alexey A.
to studying the properties of population dynamics systems using methods of numerical optimization
Design and computer research of a nonlinear stochastic models describing the dynamics of interacting populations
Demidova A.V., Druzhinina O.V., Masina O.N., Shcherbakov A.V.
. The transition to the corresponding stochastic models is made. In stochastic cases, the method of constructing

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