Igor Prigorniy
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Igor Prigorniy has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Andrey Borzunov
Lomonosov Moscow State University, Moscow, Russia
1 shared publication - Dmitry V. Lukyanenko
Faculty of Physics, Lomonosov Moscow State University, Moscow, Russia
1 shared publication - Maxim Shishlenin
Sobolev Institute of Mathematics, Institute of Computational Mathematics and Mathematical Geophysics, Novosibirsk, Russia
1 shared publication - Tatyana Yeleskina
Faculty of Physics, Lomonosov Moscow State University, Baku Branch, Baku 1143, Azerbaijan
1 shared publication - Temur Isaev
Department of Mathematics, Faculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
1 shared publication
Latest Publications
- Open Access
- 9 February 2021
- Published by MDPI AG in Mathematics
- Vol. 9 (4) , 342
Inverse Problem of Recovering the Initial Condition for a Nonlinear Equation of the Reaction–Diffusion–Advection Type by Data Given on the Position of a Reaction Front with a Time Delay
In this paper, approaches to the numerical recovering of the initial condition in the inverse problem for a nonlinear singularly perturbed reaction–diffusion–advection equation are considered. The feature of the formulation of the inverse problem is the use of additional information about the value of the solution of the equation at the known position of a reaction front, measured experimentally with a delay relative to the initial moment of time. In this case, for the numerical solution of the inverse problem, the gradient method of minimizing the cost functional is applied. In the case when only the position of the reaction front is known, the method of deep machine learning is applied. Numerical experiments demonstrated the possibility of solving such kinds of considered inverse problems.
- 9 February 2021
- Published by MDPI AG in Mathematics
- Vol. 9 (4) , 342
Open AccessInverse Problem of Recovering the Initial Condition for a Nonlinear Equation of the Reaction–Diffusion–Advection Type by Data Given on the Position of a Reaction Front with a Time Delay
In this paper, approaches to the numerical recovering of the initial condition in the inverse problem for a nonlinear singularly perturbed reaction–diffusion–advection equation are considered. The feature of the formulation of the inverse problem is the use of additional information about the value of the solution of the equation at the known position of a reaction front, measured experimentally with a delay relative to the initial moment of time. In this case, for the numerical solution of the inverse problem, the gradient method of minimizing the cost functional is applied. In the case when only the position of the reaction front is known, the method of deep machine learning is applied. Numerical experiments demonstrated the possibility of solving such kinds of considered inverse problems.