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Roads are a shared resource which can be used either by drivers and autonomous vehicles. Since the total number of vehicles increases annually, each considered vehicle spends more time in traffic jams, and thus the total travel time prolongs. The main purpose while planning the road system is to reduce the time spent on traveling. The optimization of transportation networks is a current goal, thus the formation of traffic flows by creating certain ligaments of the roads is of high importance. The Braess paradox states the existence of a network where the construction of a new edge leads to the increase of traveling time. The objective of this paper is to propose various solutions to the Braess paradox in the presence of autonomous vehicles. One of the methods of solving transportation topology problems is to introduce artificial restrictions on traffic. As an example of such restrictions, this article considers designated lanes which are available only for a certain type of vehicles. Designated lanes have their own location in the network and operating conditions. This article observes the most common two-roads traffic situations, analyzes them using analytical and numerical methods and presents the model of optimal traffic flow distribution, which considers different ways of lanes designation on isolated transportation networks. It was found that the modeling of designated lanes eliminates Braess’ paradox and optimizes the total traveling time. The solutions were shown on artificial networks and on the real-life example. A modeling algorithm for Braess network was proposed and its correctness was verified using the real-life example.

The paper describes a free software for research in the field of holomorphic dynamics based on the computational capabilities of the MATLAB environment. The software allows constructing not only single complex-valued mappings, but also their collectives as linearly connected, on a square or hexagonal lattice. In the first case, analogs of the Julia set (in the form of escaping points with color indication of the escape velocity), Fatou (with chaotic dynamics highlighting), and the Mandelbrot set generated by one of two free parameters are constructed. In the second case, only the dynamics of a cellular automaton with a complex-valued state of the cells and of all the coefficients in the local transition function is considered. The abstract nature of object-oriented programming makes it possible to combine both types of calculations within a single program that describes the iterated dynamics of one object.

The presented software provides a set of options for the field shape, initial conditions, neighborhood template, and boundary cells neighborhood features. The mapping display type can be specified by a regular expression for the MATLAB interpreter. This paper provides some UML diagrams, a short introduction to the user interface, and some examples.

The following cases are considered as example illustrations containing new scientific knowledge:

1) a linear fractional mapping in the form $Az^{n} +B/z^{n} $, for which the cases $n=2$, $4$, $n>1$, are known. In the portrait of the Fatou set, attention is drawn to the characteristic (for the classical quadratic mapping) figures of <>, showing short-period regimes, components of conventionally chaotic dynamics in the sea;

2) for the Mandelbrot set with a non-standard position of the parameter in the exponent $z(t+1)\Leftarrow z(t)^{\mu } $ sketch calculations reveal some jagged structures and point clouds resembling Cantor's dust, which are not Cantor's bouquets that are characteristic for exponential mapping. Further detailing of these objects with complex topology is required.

The paper presents a numerical solution to the heat wave motion problem for a degenerate second-order nonlinear parabolic equation with a source term. The nonlinearity is conditioned by the power dependence of the heat conduction coefficient on temperature. The problem for the case of two spatial variables is considered with the boundary condition specifying the heat wave motion law. A new solution algorithm based on an expansion in radial basis functions and the boundary element method is proposed. The solution is constructed stepwise in time with finite difference time approximation. At each time step, a boundary value problem for the Poisson equation corresponding to the original equation at a fixed time is solved. The solution to this problem is constructed iteratively as the sum of a particular solution to the nonhomogeneous equation and a solution to the corresponding homogeneous equation satisfying the boundary conditions. The homogeneous equation is solved by the boundary element method. The particular solution is sought by the collocation method using inhomogeneity expansion in radial basis functions. The calculation algorithm is optimized by parallelizing the computations. The algorithm is implemented as a program written in the C++ language. The parallel computations are organized by using the OpenCL standard, and this allows one to run the same parallel code either on multi-core CPUs or on graphic CPUs. Test cases are solved to evaluate the effectiveness of the proposed solution method and the correctness of the developed computational technique. The calculation results are compared with known exact solutions, as well as with the results we obtained earlier. The accuracy of the solutions and the calculation time are estimated. The effectiveness of using various systems of radial basis functions to solve the problems under study is analyzed. The most suitable system of functions is selected. The implemented complex computational experiment shows higher calculation accuracy of the proposed new algorithm than that of the previously developed one.

A direct algorithm for solving a linear programming problem (LP), given in canonical form, is considered. The algorithm consists of two successive stages, in which the following LP problems are solved by a direct method: a non-degenerate auxiliary problem at the first stage and some problem equivalent to the original one at the second. The construction of the auxiliary problem is based on a multiplicative version of the Gaussian exclusion method, in the very structure of which there are possibilities: identification of incompatibility and linear dependence of constraints; identification of variables whose optimal values are obviously zero; the actual exclusion of direct variables and the reduction of the dimension of the space in which the solution of the original problem is determined. In the process of actual exclusion of variables, the algorithm generates a sequence of multipliers, the main rows of which form a matrix of constraints of the auxiliary problem, and the possibility of minimizing the filling of the main rows of multipliers is inherent in the very structure of direct methods. At the same time, there is no need to transfer information (basis, plan and optimal value of the objective function) to the second stage of the algorithm and apply one of the ways to eliminate looping to guarantee final convergence.

Two variants of the algorithm for solving the auxiliary problem in conjugate canonical form are presented. The first one is based on its solution by a direct algorithm in terms of the simplex method, and the second one is based on solving a problem dual to it by the simplex method. It is shown that both variants of the algorithm for the same initial data (inputs) generate the same sequence of points: the basic solution and the current dual solution of the vector of row estimates. Hence, it is concluded that the direct algorithm is an algorithm of the simplex method type. It is also shown that the comparison of numerical schemes leads to the conclusion that the direct algorithm allows to reduce, according to the cubic law, the number of arithmetic operations necessary to solve the auxiliary problem, compared with the simplex method. An estimate of the number of iterations is given.

This paper presents algorithm for modeling set of trajectories of non-stationary time series, based on a numerical scheme for approximating the sample density of the distribution function in a problem with fixed ends, when the initial distribution for a given number of steps transforms into a certain final distribution, so that at each step the semigroup property of solving the Liouville equation is satisfied. The model makes it possible to numerically construct evolving densities of distribution functions during random switching of states of the system generating the original time series.

The main problem is related to the fact that with the numerical implementation of the left-hand differential derivative in time, the solution becomes unstable, but such approach corresponds to the modeling of evolution. An integrative approach is used while choosing implicit stable schemes with “going into the future”, this does not match the semigroup property at each step. If, on the other hand, some real process is being modeled, in which goal-setting presumably takes place, then it is desirable to use schemes that generate a model of the transition process. Such model is used in the future in order to build a predictor of the disorder, which will allow you to determine exactly what state the process under study is going into, before the process really went into it. The model described in the article can be used as a tool for modeling real non-stationary time series.

Steps of the modeling scheme are described further. Fragments corresponding to certain states are selected from a given time series, for example, trends with specified slope angles and variances. Reference distributions of states are compiled from these fragments. Then the empirical distributions of the duration of the system’s stay in the specified states and the duration of the transition time from state to state are determined. In accordance with these empirical distributions, a probabilistic model of the disorder is constructed and the corresponding trajectories of the time series are modeled.

Currently, journal papers contain numerous examples of the use of heavy-tailed distributions for applied research on various complex systems. Models of extreme data are usually limited to a small set of distribution shapes that in this field of applied research historically been used. It is possible to increase the composition of the set of probability distributions shapes through comparing the measures of the distribution shapes and choosing the most suitable implementations. The example of a beta distribution of the second kind shown that the lack of definability of the moments of heavy-tailed implementations of the beta family of distributions limits the applicability of the existing classical methods of moments for studying the distributions shapes when are characterized heavy tails. For this reason, the development of new methods for comparing distributions based on quantile shape measures free from the restrictions on the shape parameters remains relevant study the possibility of constructing a space of quantile measures of shapes for comparing distributions with heavy tails. The operation purpose consists in computer research of creation possibility of space of the quantile’s measures for the comparing of distributions property with heavy tails. On the basis of computer simulation there the distributions implementations in measures space of shapes were been shown. Mapping distributions in space only of the parametrical measures of shapes has shown that the imposition of regions for heavy tails distribution made impossible compare the shape of distributions belonging to different type in the space of quantile measures of skewness and kurtosis. It is well known that shape information measures such as entropy and entropy uncertainty interval contain additional information about the shape measure of heavy-tailed distributions. In this paper, a quantile entropy coefficient is proposed as an additional independent measure of shape, which is based on the ratio of entropy and quantile uncertainty intervals. Also estimates of quantile entropy coefficients are obtained for a number of well-known heavy-tailed distributions. The possibility of comparing the distributions shapes with realizations of the beta distribution of the second kind is illustrated by the example of the lognormal distribution and the Pareto distribution. Due to mapping the position of stable distributions in the three-dimensional space of quantile measures of shapes estimate made it possible the shape parameters to of the beta distribution of the second kind, for which shape is closest to the L&eacute;vy shape. From the paper material it follows that the display of distributions in the three-dimensional space of quantile measures of the forms of skewness, kurtosis and entropy coefficient significantly expands the possibility of comparing the forms for distributions with heavy tails.

Recently, to describe various mathematical models of physical processes, fractional differential calculus has been widely used. In this regard, much attention is paid to partial differential equations of fractional order, which are a generalization of partial differential equations of integer order. In this case, various settings are possible.

Loaded differential equations in the literature are called equations containing values of a solution or its derivatives on manifolds of lower dimension than the dimension of the definitional domain of the desired function. Currently, numerical methods for solving loaded partial differential equations of integer and fractional orders are widely used, since analytical solving methods for solving are impossible. A fairly effective method for solving this kind of problem is the finite difference method, or the grid method.

We studied the initial-boundary value problem in the rectangle $\overline{D}=\{(x,\,t)\colon 0\leqslant x\leqslant l,\;0\leqslant t\leqslant T\}$ for the loaded differential heat equation with composition fractional derivative of Riemann – Liouville and Caputo – Gerasimov and with boundary conditions of the first and third kind. We have gotten an a priori assessment in differential and difference interpretations. The obtained inequalities mean the uniqueness of the solution and the continuous dependence of the solution on the input data of the problem. A difference analogue of the composition fractional derivative of Riemann – Liouville and Caputo –Gerasimov order $(2-\beta )$ is obtained and a difference scheme is constructed that approximates the original problem with the order $O\left(\tau +h^{2-\beta } \right)$. The convergence of the approximate solution to the exact one is proven at a rate equal to the order of approximation of the difference scheme.

In recent years, the use of neural network models for solving aerodynamics problems has become widespread. These models, trained on a set of previously obtained solutions, predict solutions to new problems. They are, in essence, interpolation algorithms. An alternative approach is to construct a neural network operator. This is a neural network that reproduces a numerical method used to solve a problem. It allows to find the solution in iterations. The paper considers the construction of such an operator using the UNet neural network with a spatial attention mechanism. It solves flow problems on a rectangular uniform grid that is common to a streamlined body and flow field. A correction mechanism is proposed to clarify the obtained solution. The problem of the stability of such an algorithm for solving a stationary problem is analyzed, and a comparison is made with other variants of its construction, including pushforward trick and positional encoding. The issue of selecting a set of iterations for forming a train dataset is considered, and the behavior of the solution is assessed using repeated use of a neural network operator.

A demonstration of the method is provided for the case of flow around a rounded plate with a turbulent flow, with various options for rounding, for fixed parameters of the incoming flow, with Reynolds number $\text{Re} = 10^5$ and Mach number $M = 0.15$. Since flows with these parameters of the incoming flow can be considered incompressible, only velocity components are directly studied. At the same time, the neural network model used to construct the operator has a common decoder for both velocity components. Comparison of flow fields and velocity profiles along the normal and outline of the body, obtained using a neural network operator and numerical methods, is carried out. Analysis is performed both on the plate and rounding. Simulation results confirm that the neural network operator allows finding a solution with high accuracy and stability.

The paper considers the problem of parameter identification of discrete-time linear stochastic systems in the state space with additive and multiplicative noise. It is assumed that the state and measurements equations of a discrete-time linear stochastic system depend on an unknown parameter to be identified.

A new approach to the construction of gradient parameter identification methods in the class of discrete-time linear stochastic systems with additive and multiplicative noise is presented, based on the application of modified weighted Gram – Schmidt orthogonalization (MWGS) and the discrete-time information-type filtering algorithms.

The main theoretical results of this research include: 1) a new identification criterion in terms of an extended information filter; 2) a new algorithm for calculating derivatives with respect to an uncertainty parameter in a discrete-time linear stochastic system based on an extended information LD filter using the direct procedure of modified weighted Gram – Schmidt orthogonalization; and 3) a new method for calculating the gradient of identification criteria using a “differentiated” extended information LD filter.

The advantages of this approach are that it uses MWGS orthogonalization which is numerically stable against machine roundoff errors, and it forms the basis of all the developed methods and algorithms. The information LD-filter maintains the symmetry and positive definiteness of the information matrices. The algorithms have an array structure that is convenient for computer implementation.

All the developed algorithms were implemented in MATLAB. A series of numerical experiments were carried out. The results obtained demonstrated the operability of the proposed approach, using the example of solving the problem of parameter identification for a mathematical model of a complex mechanical system.

The results can be used to develop methods for identifying parameters in mathematical models that are represented in state space by discrete-time linear stochastic systems with additive and multiplicative noise.

It is shown that the extension of base language in Zermelo–Fraenkel theory, which allows a relations on recursion function of natural argument, may lead in Set Theory to contradictive constructions on arithmetic level.