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The review considers a wide range of phenomena and problems associated with the explosion. Detailed numerical studies revealed an interesting physical effect — the formation of discrete vortex structures directly behind the front of a shock wave propagating in dense layers of a heterogeneous atmosphere. The necessity of further investigation of such phenomena and the determination of the degree of their connection with the possible development of gas-dynamic instability is shown. The brief analysis of numerous works on the thermal explosion of meteoroids during their high-speed movement in the Earth’s atmosphere is given. Much attention is paid to the development of a numerical algorithm for calculating the simultaneous explosion of several fragments of meteoroids and the features of the development of such a gas-dynamic flow are analyzed. The work shows that earlier developed algorithms for calculating explosions can be successfully used to study explosive volcanic eruptions. The paper presents and discusses the results of such studies for both continental and underwater volcanoes with certain restrictions on the conditions of volcanic activity.

The mathematical analysis is performed and the results of analytical studies of a number of important physical phenomena characteristic of explosions of high specific energy in the ionosphere are presented. It is shown that the preliminary laboratory physical modeling of the main processes that determine these phenomena is of fundamental importance for the development of sufficiently complete and adequate theoretical and numerical models of such complex phenomena as powerful plasma disturbances in the ionosphere. Laser plasma is the closest object for such a simulation. The results of the corresponding theoretical and experimental studies are presented and their scientific and practical significance is shown. The brief review of recent years on the use of laser radiation for laboratory physical modeling of the effects of a nuclear explosion on asteroid materials is given.

As a result of the analysis performed in the review, it was possible to separate and preliminarily formulate some interesting and scientifically significant questions that must be investigated on the basis of the ideas already obtained. These are finely dispersed chemically active systems formed during the release of volcanoes; small-scale vortex structures; generation of spontaneous magnetic fields due to the development of instabilities and their role in the transformation of plasma energy during its expansion in the ionosphere. It is also important to study a possible laboratory physical simulation of the thermal explosion of bodies under the influence of highspeed plasma flow, which has only theoretical interpretations.

Correct modeling of complex dynamics of urban transportation flows requires the collection of large volumes of empirical data to specify types of the modes and their identification. At the same time, setting a large number of observation posts is expensive and technically not always feasible. All this results in insufficient factographic support for the traffic control systems as well as for urban planners with the obvious consequences for the quality of their decisions. As one of the means to provide large-scale data collection at least for the qualitative situation analysis, the wide-area video cameras are used in different situation centers. There they are analyzed by human operators who are responsible for observation and control. Some video cameras provided their videos for common access, which makes them a valuable resource for transportation studies. However, there are significant problems with getting qualitative data from such cameras, which relate to the theory and practice of image processing. This study is devoted to the practical application of certain mainstream neuro-networking technologies for the estimation of essential characteristics of actual transportation flows. The problems arising in processing these data are analyzed, and their solutions are suggested. The convolution neural networks are used for tracking, and the methods for obtaining basic parameters of transportation flows from these observations are studied. The simplified neural networks are used for the preparation of training sets for the deep learning neural network YOLOv4 which is later used for the estimation of speed and density of automobile flows.

In this paper, a gravitational system is understood as a set of point bodies that interact according to Newton's law of attraction and have a negative value of the total energy. The question of the stability (nonstability) of a gravitational system of general position is discussed by direct computational experiment. A gravitational system of general position is a system in which the masses, initial positions, and velocities of bodies are chosen randomly from given ranges. A new method for the numerical solution of ordinary differential equations at large time intervals has been developed for the computational experiment. The proposed method allowed, on the one hand, to ensure the fulfillment of all conservation laws by a suitable correction of solutions, on the other hand, to use standard methods for the numerical solution of systems of differential equations of low approximation order. Within the framework of this method, the trajectory of a gravitational system in phase space is assembled from parts, the duration of each of which can be macroscopic. The constructed trajectory, generally speaking, is discontinuous, and the points of joining of individual pieces of the trajectory act as branch points. In connection with the latter circumstance, the proposed method, in part, can be attributed to the class of Monte Carlo methods. The general conclusion of a series of computational experiments has shown that gravitational systems of general position with a number of bodies of 3 or more, generally speaking, are unstable. In the framework of the proposed method, special cases of zero-equal angular momentum of a gravitational system with a number of bodies of 3 or more, as well as the problem of motion of two bodies, are specially considered. The case of numerical modeling of the dynamics of the solar system in time is considered separately. From the standpoint of computational experiments based on analytical methods, as well as direct numerical methods of high-order approximation (10 and higher), the stability of the solar system was previously demonstrated at an interval of five billion years or more. Due to the limitations on the available computational resources, the stability of the dynamics of the planets of the solar system within the framework of the proposed method was confirmed for a period of ten million years. With the help of a computational experiment, one of the possible scenarios for the disintegration of the solar systems is also considered.

An important role in the construction of mathematical models of dynamic systems is played by inverse problems, which in particular include the problem of parametric identification. Unlike classical models that operate with point values, interval models give upper and lower boundaries on the quantities under study. The paper considers an interpolation approach to solving interval problems of parametric identification of dynamic systems for the case when experimental data are represented by external interval estimates. The purpose of the proposed approach is to find such an interval estimate of the model parameters, in which the external interval estimate of the solution of the direct modeling problem would contain experimental data or minimize the deviation from them. The approach is based on the adaptive interpolation algorithm for modeling dynamic systems with interval uncertainties, which makes it possible to explicitly obtain the dependence of phase variables on system parameters. The task of minimizing the distance between the experimental data and the model solution in the space of interval boundaries of the model parameters is formulated. An expression for the gradient of the objectivet function is obtained. On a representative set of tasks, the effectiveness of the proposed approach is demonstrated.

In this work, we use analytical and numerical methods to consider the problem of the structure and dynamics of coupled localized nonlinear waves in the sine-Gordon model with three identical attractive extended “impurities”, which are modeled by spatial inhomogeneity of the periodic potential. Two possible types of coupled nonlinear localized waves are found: breather and soliton. The influence of system parameters and initial conditions on the structure, amplitude, and frequency of localized waves was analyzed. Associated oscillations of localized waves of the breather type as in the case of point impurities, are the sum of three harmonic oscillations: in-phase, in-phase-antiphase and antiphase type. Frequency analysis of impurity-localized waves that were obtained during a numerical experiment was performed using discrete Fourier transform. To analyze localized breather-type waves, the numerical finite difference method was used. To carry out a qualitative analysis of the obtained numerical results, the problem was solved analytically for the case of small amplitudes of oscillations localized on impurities. It is shown that, for certain impurity parameters (depth and width), it is possible to obtain localized solitontype waves. The ranges of values of the system parameters in which localized waves of a certain type exist, as well as the region of transition from breather to soliton types of oscillations, have been found. The values of the depth and width of the impurity at which a transition from the breather to the soliton type of localized oscillations is observed were determined. Various scenarios of soliton-type oscillations with negative and positive amplitude values for all three impurities, as well as mixed cases, were obtained and considered. It is shown that in the case when the distance between impurities much less than one, there is no transition region where which the nascent breather, after losing energy through radiation, transforms into a soliton. It is shown that the considered model can be used, for example, to describe the dynamics of magnetization waves in multilayer magnets.

A numerical method is proposed that approximates the equations of the dynamics of a weakly compressible viscous flow in the presence of a polymer component of the flow. The behavior of the flow under the influence of a static external periodic force in a periodic square cell is investigated. The methodology is based on a hybrid approach. The hydrodynamics of the flow is described by a system of Navier – Stokes equations and is numerically approximated by the linearized Godunov method. The polymer field is described by a system of equations for the vector of stretching of polymer molecules $\bf R$, which is numerically approximated by the Kurganov – Tedmor method. The choice of model relationships in the development of a numerical methodology and the selection of modeling parameters made it possible to qualitatively model and study the regime of elastic turbulence at low Reynolds $Re \sim 10^{-1}$. The polymer solution flow dynamics equations differ from the Newtonian fluid dynamics equations by the presence on the right side of the terms describing the forces acting on the polymer component part. The proportionality coefficient $A$ for these terms characterizes the backward influence degree of the polymers number on the flow. The article examines in detail how the flow and its characteristics change depending on the given coefficient. It is shown that with its growth, the flow becomes more chaotic. The flow energy spectra and the spectra of the polymers stretching field are constructed for different values of $A$. In the spectra, an inertial sub-range of the energy cascade is traced for the flow velocity with an indicator $k \sim −4$, for the cascade of polymer molecules stretches with an indicator $−1.6$.

Modern GPUs are massively-parallel processors, offering substantial amount of computational power in energy-efficient package. We discuss the benefits of utilizing this computing power for modeling problems in bio-informatics, such as molecular dynamics, quantum chemistry and sequence analysis.

Тhe new test-signals forming method for correlation identification of a nonlinear system based on Lee–Shetzen cross-correlation approach is developed and tested. Numerical Gauss–Newton algorithm is applied to correct autocorrelation functions of test signals. The achieved test-signals have length less than 40 000 points and allow to measure the 2nd order Wiener kernels with a linear resolution up to 32 points, the 3rd order Wiener kernels with a linear resolution up to 12 points and the 4th order Wiener kernels with a linear resolution up to 8 points.

Stochastically forced discrete dynamical systems are considered. Using first approximation systems, we study dynamics of deviations of stochastic solutions from deterministic equilibria. Necessary and sufficient conditions of the existence of stable stationary solutions of equations for mean-square deviations are derived. Stationary values of these mean-square deviations are used for the estimations of the dispersion of random states nearby stable equilibria and analysis of noise-induced transitions. Constructive application of the suggested technique to the analysis of various stochastic regimes in Ricker population model with Allee effect is demonstrated.