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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.

The paper presents the results of theoretical investigation of the peculiarities of the quasi-harmonic signal’s phase statistical distribution, while the quasi-harmonic signal is formed as a result of the Gaussian noise impact on the initially harmonic signal. The revealed features of the phase distribution became a basis for the original technique elaborated for estimating the parameters of the initial, undistorted signal. It has been shown that the task of estimation of the initial phase value can be efficiently solved by calculating the magnitude of the mathematical expectation of the results of the phase sampled measurements, while for solving the task of estimation of the second parameter — the signal level respectively to the noise level — the dependence of the phase sampled measurements variance upon the sough-for parameter is proposed to be used. For solving this task the analytical formulas having been obtained in explicit form for the moments of lower orders of the phase distribution, are applied. A new approach to quasi-harmonic signal’s parameters estimation based on the method of moments has been developed and substantiated. In particular, the application of this method ensures a high-precision measuring the amplitude characteristics of a signal by means of the phase measurements only. The numerical results obtained by means of conducted computer simulation of the elaborated technique confirm both the theoretical conclusions and the method’s efficiency. The existence and the uniqueness of the task solution by the method of moments is substantiated. It is shown that the function that describes the dependence of the phase second central moment on the sough-for parameter, is a monotonically decreasing and thus the single-valued function. The developed method may be of interest for solving a wide range of scientific and applied tasks, connected with the necessity of estimation of both the signal level and the phase value, in such areas as data processing in systems of medical diagnostic visualization, radio-signals processing, radio-physics, optics, radio-navigation and metrology.

We now review the main points of mean-field approximation (MFA) in its application to multicomponent stochastic reaction-diffusion systems.

We present the chemical reaction model under study — brusselator. We write the kinetic equations of reaction supplementing them with terms that describe the diffusion of the intermediate components and the fluctuations of the concentrations of the initial products. We simulate the fluctuations as random Gaussian homogeneous and spatially isotropic fields with zero means and spatial correlation functions with a non-trivial structure. The model parameter values correspond to a spatially-inhomogeneous ordered state in the deterministic case.

In the MFA we derive single-site two-dimensional nonlinear self-consistent Fokker–Planck equation in the Stratonovich's interpretation for spatially extended stochastic brusselator, which describes the dynamics of probability distribution density of component concentration values of the system under consideration. We find the noise intensity values appropriate to two types of Fokker–Planck equation solutions: solution with transient bimodality and solution with the multiple alternation of unimodal and bimodal types of probability density. We study numerically the probability density dynamics and time behavior of variances, expectations, and most probable values of component concentrations at various noise intensity values and the bifurcation parameter in the specified region of the problem parameters.

Beginning from some value of external noise intensity inside the ordered phase disorder originates existing for a finite time, and the higher the noise level, the longer this disorder “embryo” lives. The farther away from the bifurcation point, the lower the noise that generates it and the narrower the range of noise intensity values at which the system evolves to the ordered, but already a new statistically steady state. At some second noise intensity value the intermittency of the ordered and disordered phases occurs. The increasing noise intensity leads to the fact that the order and disorder alternate increasingly.

Thus, the scenario of the noise induced order–disorder transition in the system under study consists in the intermittency of the ordered and disordered phases.

The paper is devoted to convex-concave saddle point problems where the objective is a sum of a large number of functions. Such problems attract considerable attention of the mathematical community due to the variety of applications in machine learning, including adversarial learning, adversarial attacks and robust reinforcement learning, to name a few. The individual functions in the sum usually represent losses related to examples from a data set. Additionally, the formulation admits a possibly nonsmooth composite term. Such terms often reflect regularization in machine learning problems. We assume that the dimension of one of the variable groups is relatively small (about a hundred or less), and the other one is large. This case arises, for example, when one considers the dual formulation for a minimization problem with a moderate number of constraints. The proposed approach is based on using Vaidya’s cutting plane method to minimize with respect to the outer block of variables. This optimization algorithm is especially effective when the dimension of the problem is not very large. An inexact oracle for Vaidya’s method is calculated via an approximate solution of the inner maximization problem, which is solved by the accelerated variance reduced algorithm Katyusha. Thus, we leverage the structure of the problem to achieve fast convergence. Separate complexity bounds for gradients of different components with respect to different variables are obtained in the study. The proposed approach is imposing very mild assumptions about the objective. In particular, neither strong convexity nor smoothness is required with respect to the low-dimensional variable group. The number of steps of the proposed algorithm as well as the arithmetic complexity of each step explicitly depend on the dimensionality of the outer variable, hence the assumption that it is relatively small.

The currently performed mathematical and computer modeling of thermal processes in technical systems is based on an assumption that all the parameters determining thermal processes are fully and unambiguously known and identified (i.e., determined). Meanwhile, experience has shown that parameters determining the thermal processes are of undefined interval-stochastic character, which in turn is responsible for the intervalstochastic nature of thermal processes in the electronic system. This means that the actual temperature values of each element in an technical system will be randomly distributed within their variation intervals. Therefore, the determinative approach to modeling of thermal processes that yields specific values of element temperatures does not allow one to adequately calculate temperature distribution in electronic systems. The interval-stochastic nature of the parameters determining the thermal processes depends on three groups of factors: (a) statistical technological variation of parameters of the elements when manufacturing and assembling the system; (b) the random nature of the factors caused by functioning of an technical system (fluctuations in current and voltage; power, temperatures, and flow rates of the cooling fluid and the medium inside the system); and (c) the randomness of ambient parameters (temperature, pressure, and flow rate). The interval-stochastic indeterminacy of the determinative factors in technical systems is irremediable; neglecting it causes errors when designing electronic systems. A method that allows modeling of unsteady interval-stochastic thermal processes in technical systems (including those upon interval indeterminacy of the determinative parameters) is developed in this paper. The method is based on obtaining and further solving equations for the unsteady statistical measures (mathematical expectations, variances and covariances) of the temperature distribution in an technical system at given variation intervals and the statistical measures of the determinative parameters. Application of the elaborated method to modeling of the interval-stochastic thermal process in a particular electronic system is considered.

This article presents the results of an analytical and computer study of the chaotic evolution of a regular velocity field generated by a large-scale harmonic forcing. The authors obtained an analytical solution for the flow stream function and its derivative quantities (velocity, vorticity, kinetic energy, enstrophy and palinstrophy). Numerical modeling of the flow evolution was carried out using the OpenFOAM software package based on incompressible model, as well as two inhouse implementations of CABARET and McCormack methods employing nearly incompressible formulation. Calculations were carried out on a sequence of nested meshes with 64², 128², 256², 512², 1024² cells for two characteristic (asymptotic) Reynolds numbers characterizing laminar and turbulent evolution of the flow, respectively. Simulations show that blow-up of the analytical solution takes place in both cases. The energy characteristics of the flow are discussed relying upon the energy curves as well as the dissipation rates. For the fine mesh, this quantity turns out to be several orders of magnitude less than its hydrodynamic (viscous) counterpart. Destruction of the regular flow structure is observed for any of the numerical methods, including at the late stages of laminar evolution, when numerically obtained distributions are close to analytics. It can be assumed that the prerequisite for the development of instability is the error accumulated during the calculation process. This error leads to unevenness in the distribution of vorticity and, as a consequence, to the variance vortex intensity and finally leads to chaotization of the flow. To study the processes of vorticity production, we used two integral vorticity-based quantities — integral enstrophy ($\zeta$) and palinstrophy $(P)$. The formulation of the problem with periodic boundary conditions allows us to establish a simple connection between these quantities. In addition, $\zeta$ can act as a measure of the eddy resolution of the numerical method, and palinstrophy determines the degree of production of small-scale vorticity.

The effectiveness of communication and data transmission systems (CSiPS), which are an integral part of modern systems in almost any field of science and technology, largely depends on the stability of the frequency of the generated signals. The signals generated in the CSiPD can be considered as processes, the frequency of which changes under the influence of a combination of external influences. Changing the frequency of the signals leads to a decrease in the signal-tonoise ratio (SNR) and, consequently, a deterioration in the characteristics of the signal-to-noise ratio, such as the probability of a bit error and bandwidth. It is most convenient to consider the description of such changes in the frequency of signals as random processes, the apparatus of which is widely used in the construction of mathematical models describing the functioning of systems and devices in various fields of science and technology. Moreover, in many cases, the characteristics of a random process, such as the distribution law, mathematical expectation, and variance, may be unknown or known with errors that do not allow us to obtain estimates of the signal parameters that are acceptable in accuracy. The article proposes an algorithm for solving the problem of determining the characteristics of a random process (signal frequency) based on a set of samples of its frequency, allowing to determine the sample mean, sample variance and the distribution law of frequency deviations in the general population. The basis of this algorithm is the comparison of the values of the observed random process measured over a certain time interval with a set of the same number of random values formed on the basis of model distribution laws. Distribution laws based on mathematical models of these systems and devices or corresponding to similar systems and devices can be considered as model distribution laws. When forming a set of random values for the accepted model distribution law, the sample mean value and variance obtained from the measurement results of the observed random process are used as mathematical expectation and variance. The feature of the algorithm is to compare the measured values of the observed random process ordered in ascending or descending order and the generated sets of values in accordance with the accepted models of distribution laws. The results of mathematical modeling illustrating the application of this algorithm are presented.

A cluster method of mathematical modeling of interval-stochastic thermal processes in complex electronic systems (ES), is developed. In the cluster method, the construction of a complex ES is represented in the form of a thermal model, which is a system of clusters, each of which contains a core that combines the heat-generating elements falling into a given cluster, the cluster shell and a medium flow through the cluster. The state of the thermal process in each cluster and every moment of time is characterized by three interval-stochastic state variables, namely, the temperatures of the core, shell, and medium flow. The elements of each cluster, namely, the core, shell, and medium flow, are in thermal interaction between themselves and elements of neighboring clusters. In contrast to existing methods, the cluster method allows you to simulate thermal processes in complex ESs, taking into account the uneven distribution of temperature in the medium flow pumped into the ES, the conjugate nature of heat exchange between the medium flow in the ES, core and shells of clusters, and the intervalstochastic nature of thermal processes in the ES, caused by statistical technological variation in the manufacture and installation of electronic elements in ES and random fluctuations in the thermal parameters of the environment. The mathematical model describing the state of thermal processes in a cluster thermal model is a system of interval-stochastic matrix-block equations with matrix and vector blocks corresponding to the clusters of the thermal model. The solution to the interval-stochastic equations are statistical measures of the state variables of thermal processes in clusters - mathematical expectations, covariances between state variables and variance. The methodology for applying the cluster method is shown on the example of a real ES.

The paper proposes an approach to simulation of a sport game, consisting of a discrete set of separate competitions. According to this approach, such a competition is considered as a random processes, generally — a non-Markov’s one. At first we treat the flow of the game as a Markov’s process, obtaining recursive relationship between the probabilities of achieving certain states of score in a tennis match, as well as secondary indicators of the game, such as expectation and variance of the number of serves to finish the game. Then we use a simulation system, modeling the match, to allow an arbitrary change of the probabilities of the outcomes in the competitions that compose the match. We, for instance, allow the probabilities to depend on the results of previous competitions. Therefore, this paper deals with a modification of the model, previously proposed by the authors for sports games with continuous time.

The proposed approach allows to evaluate not only the probability of the final outcome of the match, but also the probabilities of reaching each of the possible intermediate results, as well as secondary indicators of the game, such as the number of separate competitions it takes to finish the match. The paper includes a detailed description of the construction of a simulation system for a game of a tennis match. Then we consider simulating a set and the whole tennis match by analogy. We show some statements concerning fairness of tennis serving rules, understood as independence of the outcome of a competition on the right to serve first. We perform simulation of a cancelled ATP series match, obtaining its most probable intermediate and final outcomes for three different possible variants of the course of the match.

The main result of this paper is the developed method of simulation of the match, applicable not only to tennis, but also to other types of sports games with discrete time.

The paper considers the problem of estimating the parameters of time series described by regression models with Markov switching of two regimes at random instants of time with independent Gaussian noise. For the solution, we propose a variant of the EM algorithm based on the iterative procedure, during which an estimation of the regression parameters is performed for a given sequence of regime switching and an evaluation of the switching sequence for the given parameters of the regression models. In contrast to the well-known methods of estimating regression parameters in the models with Markov switching, which are based on the calculation of a posteriori probabilities of discrete states of the switching sequence, in the paper the estimates are calculated of the switching sequence, which are optimal by the criterion of the maximum of a posteriori probability. As a result, the proposed algorithm turns out to be simpler and requires less calculations. Computer modeling allows to reveal the factors influencing accuracy of estimation. Such factors include the number of observations, the number of unknown regression parameters, the degree of their difference in different modes of operation, and the signal-to-noise ratio which is associated with the coefficient of determination in regression models. The proposed algorithm is applied to the problem of estimating parameters in regression models for the rate of daily return of the RTS index, depending on the returns of the S&P 500 index and Gazprom shares for the period from 2013 to 2018. Comparison of the estimates of the parameters found using the proposed algorithm is carried out with the estimates that are formed using the EViews econometric package and with estimates of the ordinary least squares method without taking into account regimes switching. The account of regimes switching allows to receive more exact representation about structure of a statistical dependence of investigated variables. In switching models, the increase in the signal-to-noise ratio leads to the fact that the differences in the estimates produced by the proposed algorithm and using the EViews program are reduced.