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			卷 65, 编号 9 (2025)
General numerical methods
DIFFERENCE SCHEMES BASED ON EXPONENTIALLY CONVERGING QUADRATURES FOR THE CAUCHY INTEGRAL
摘要
 1469–1478
				
					1469–1478
				
						 
			
				 
				
			
		Partial Differential Equations
PRINCIPLES OF DUALISM IN THE THEORY OF SOLUTIONS OF INFINITE-DIMENSIONAL DIFFERENTIAL EQUATIONS DEPENDING ON EXISTING TYPES OF SYMMETRIES
摘要
 1479–1504
				
					1479–1504
				
						 
			
				 
				
			
		CONVERGENCE OF EIGENELEMENTS OF A STEKLOV-TYPE BOUNDARY VALUE PROBLEM FOR THE LAME OPERATOR IN A SEMI-CYLINDER WITH A SMALL CAVITY
摘要
 1505-1517
				
					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
摘要
 1518-1524
				
					1518-1524
				
						 
			
				 
				
			
		Mathematical physics
High-precision difference boundary conditions for bicompact circuits split by transfer processes
摘要
 1525-1539
				
					1525-1539
				
						 
			
				 
				
			
		ANALYSIS OF PERTURBATION COEFFICIENTS IN THE PROBLEM OF FILTERING NONLINEAR DISTORTIONS IN FIBER OPTICS
摘要
 1540-1555
				
					1540-1555
				
						 
			
				 
				
			
		ON THE DEFINITION OF PLANE-PARALLEL MEDIUM REFLECTION AND TRANSMISSION OPERATORS
摘要
 1556-1559
				
					1556-1559
				
						 
			
				 
				
			
		A NOTE ON THE APPLICATION OF THE CHARACTERISTIC FUNCTION TO THE CALCULATION OF INERTIA INTEGRALS OF A RIGID BODY
摘要
 1560-1565
				
					1560-1565
				
						 
			
				 
				
			
		Computer science
NUMERICAL-ANALYTICAL METHOD FOR ESTIMATING SIGNAL PARAMETERS ON A SET OF ALTERNATIVE GRIDS UNDER UNCERTAINTY CONDITIONS
摘要
 1566–1580
				
					1566–1580
				
						 
			
				 
				
			
		SEPARABLE PHYSICS-INFORMED NEURAL NETWORKS FOR SOLVING ELASTICITY PROBLEMS
摘要
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
				
					1581-1596
				
						 
			
				 
				
			
		A DECOMPOSITION APPROACH ON THE BASE OF BROWNIAN ITERATION FOR THE LINEAR PROGRAMMING WHERE ALL BASIS MATRICES ARE M-MATRIX
摘要
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
				
					1597-1606
				
						 
			
				 
				
			
		 
						 
						 
						 
						 
					 
				


