Computational Mathematics and Mathematical Physics
ISSN (print): 0044-4669
Founders: Russian Academy of Sciences, Federal Research Center IU named after. A. A. Dorodnitsyna RAS
Editor-in-Chief: Evgeniy Evgenievich Tyrtyshnikov, Academician of the Russian Academy of Sciences, Doctor of Physics and Mathematics sciences, professor
Frequency / access: 12 issues per year / Subscription
Included in: White List (2nd level), Higher Attestation Commission list, RISC, Mathnet.ru
Media registration certificate: № 0110141 от 04.02.1993
Current Issue
Vol 65, No 9 (2025)
General numerical methods
DIFFERENCE SCHEMES BASED ON EXPONENTIALLY CONVERGING QUADRATURES FOR THE CAUCHY INTEGRAL
Abstract
1469–1478
Partial Differential Equations
PRINCIPLES OF DUALISM IN THE THEORY OF SOLUTIONS OF INFINITE-DIMENSIONAL DIFFERENTIAL EQUATIONS DEPENDING ON EXISTING TYPES OF SYMMETRIES
Abstract
1479–1504
CONVERGENCE OF EIGENELEMENTS OF A STEKLOV-TYPE BOUNDARY VALUE PROBLEM FOR THE LAME OPERATOR IN A SEMI-CYLINDER WITH A SMALL CAVITY
Abstract
1505-1517
ON THE EXISTENCE AND UNIQUENESS OF THE SOLUTION OF AN INTEGRO–DIFFERENTIAL EQUATION IN THE PROBLEM OF DIFFRACTION OF AN ELECTROMAGNETIC WAVE ON AN INHOMOGENEOUS DIEJECTRIC BODY COATED WITH GRAPHENE
Abstract
1518-1524
Mathematical physics
High-precision difference boundary conditions for bicompact circuits split by transfer processes
Abstract
1525-1539
ANALYSIS OF PERTURBATION COEFFICIENTS IN THE PROBLEM OF FILTERING NONLINEAR DISTORTIONS IN FIBER OPTICS
Abstract
1540-1555
1556-1559
A NOTE ON THE APPLICATION OF THE CHARACTERISTIC FUNCTION TO THE CALCULATION OF INERTIA INTEGRALS OF A RIGID BODY
Abstract
1560-1565
Computer science
NUMERICAL-ANALYTICAL METHOD FOR ESTIMATING SIGNAL PARAMETERS ON A SET OF ALTERNATIVE GRIDS UNDER UNCERTAINTY CONDITIONS
Abstract
1566–1580
SEPARABLE PHYSICS-INFORMED NEURAL NETWORKS FOR SOLVING ELASTICITY PROBLEMS
Abstract
Abstract –A method for solving elasticity problems based on separable physics-informed neural networks (SPINN) in conjunction with the deep energy method (DEM) is presented. Numerical experiments have been carried out for a number of problems showing that this method has a significantly higher convergence rate and accuracy than the vanilla physics-informed neural networks (PINN) and even SPINN based on a system of partial differential equations (PDEs). In addition, using the SPINN in the framework of DEM approach it is possible to solve problems of the linear theory of elasticity on complex geometries, which is unachievable with the help of PINNs in frames of partial differential equations. Considered problems are very close to the industrial problems in terms of geometry, loading, and material parameters. Bibl. 61. Fig. 6. Tabl. 8.
1581-1596
A DECOMPOSITION APPROACH ON THE BASE OF BROWNIAN ITERATION FOR THE LINEAR PROGRAMMING WHERE ALL BASIS MATRICES ARE M-MATRIX
Abstract
A new scheme for solving a problem for linear programming is proposed. The main property that distinguishes the considered problem is that the basis sub-matrices of its matrix are composed of only M-matrices. Based on the possibility created by this property, a matrix game with the same structure and size as its matrix is set against the given problem, and the possibility of constructing the optimal basis of the problem by partially executing the Brownian iteration leading to the optimal strategy of the second player is shown. Thus, we decompose the solution of the problem into the execution of a finite number of Brownian iterations. The areas of application of the solution scheme are shown. A numerical example illustrates the scheme. The possibility of replacing the game matrix with an integer-element matrix is also shown. This property allows Brownian iteration to be performed exactly. Bibl. 38.
1597-1606


