All issues

The paper contains the analysis of the domestic publications issued in 2013–2017 years and devoted to cellular automata. The most of them concern on mathematical modeling. Scientometric schedules for 1990–2017 years have proved relevance of subject. The review allows to allocate the main personalities and the scientific directions/schools in modern Russian science, to reveal their originality or secondness in comparison with world science. Due to the authors choice of national publications basis instead of world, the paper claims the completeness and the fact is that about 200 items from the checked 526 references have an importance for science.

In the Annex to the review provides preliminary information about CA — the Game of Life, a theorem about gardens of Eden, elementary CAs (together with the diagram of de Brujin), block Margolus’s CAs, alternating CAs. Attention is paid to three important for modeling semantic traditions of von Neumann, Zuse and Zetlin, as well as to the relationship with the concepts of neural networks and Petri nets. It is allocated conditional 10 works, which should be familiar to any specialist in CA. Some important works of the 1990s and later are listed in the Introduction.

Then the crowd of publications is divided into categories: the modification of the CA and other network models (29 %), Mathematical properties of the CA and the connection with mathematics (5 %), Hardware implementation (3 %), Software implementation (5 %), Data Processing, recognition and Cryptography (8 %), Mechanics, physics and chemistry (20 %), Biology, ecology and medicine (15 %), Economics, urban studies and sociology (15 %). In parentheses the share of subjects in the array are indicated. There is an increase in publications on CA in the humanitarian sphere, as well as the emergence of hybrid approaches, leading away from the classic CA definition.

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 system of linear hyperbolic kinetic equations with the relaxation term of Bhatnagar–Gross–Krook type for modelling of linear diffusion processes by the lattice Boltzmann method is considered. The coefficients of the equations depend on the discrete velocities from the pattern in velocity space. The system may be considered as an alternative mathematical model of the linear diffusion process. The cases of widely-used patterns on speed variables are considered. The case of parametric coefficients takes into account. By application of the method of Chapman–Enskog asymptotic expansion it is obtained, that the system may be reduced to the linear diffusion equation. The expression of the diffusion coefficient is obtained. As a result of the analysis of this expression, the existence of numerical diffusion in solutions obtained by application of lattice Boltzmann equations is demonstrated. Stability analysis is based on the investigation of wave modes defined by the solutions of hyperbolic system. In the cases of some one-dimensional patterns stability analysis may be realized analytically. In other cases the algorithm of numerical stability investigation is proposed. As a result of the numerical investigation stability of the solutions is shown for a wide range of input parameters. The sufficiency of the positivity of the relaxation parameter for the stability of solutions is demonstrated. The dispersion of the solutions, which is not realized for a linear diffusion equation, is demonstrated analytically and numerically for a wide range of the parameters. But the dispersive wave modes can be damped as an asymptotically stable solutions and the behavior of the solution is similar to the solution of linear diffusion equation. Numerical schemes, obtained from the proposed systems by various discretization techniques may be considered as a tool for computer modelling of diffusion processes, or as a solver for stationary problems and in applications of the splitting lattice Boltzmann method. Obtained results may be used for the comparison of the theoretical properties of the difference schemes of the lattice Boltzmann method for modelling of linear diffusion.

Different versions of the shifting mode of reproduction models describe set of the macroeconomic production subsystems interacting with each other, to each of which there corresponds the household. These subsystems differ among themselves on age of the fixed capital used by them as they alternately stop production for its updating by own forces (for repair of the equipment and for introduction of the innovations increasing production efficiency). It essentially distinguishes this type of models from the models describing the mode of joint reproduction in case of which updating of fixed capital and production of a product happen simultaneously. Models of the shifting mode of reproduction allow to describe mechanisms of such phenomena as cash circulations and amortization, and also to describe different types of monetary policy, allow to interpret mechanisms of economic growth in a new way. Unlike many other macroeconomic models, model of this class in which the subsystems competing among themselves serially get an advantage in comparison with the others because of updating, essentially not equilibrium. They were originally described as a systems of ordinary differential equations with abruptly varying coefficients. In the numerical calculations which were carried out for these systems depending on parameter values and initial conditions both regular, and not regular dynamics was revealed. This paper shows that the simplest versions of this model without the use of additional approximations can be represented in a discrete form (in the form of non-linear mappings) with different variants (continuous and discrete) financial flows between subsystems (interpreted as wages and subsidies). This form of representation is more convenient for receipt of analytical results as well as for a more economical and accurate numerical calculations. In particular, its use allowed to determine the entry conditions corresponding to coordinated and sustained economic growth without systematic lagging in production of a product of one subsystems from others.

When modeling turbulent flows in practical applications, it is often necessary to carry out a series of calculations of bodies of similar topology. For example, bodies that differ in the shape of the fairing. The use of convolutional neural networks allows to reduce the number of calculations in a series, restoring some of them based on calculations already performed. The paper proposes a method that allows to apply a convolutional neural network regardless of the method of constructing a computational mesh. To do this, the flow field is reinterpolated to a uniform mesh along with the body itself. The geometry of the body is set using the signed distance function and masking. The restoration of the flow field based on part of the calculations for similar geometries is carried out using a neural network of the UNet type with a spatial attention mechanism. The resolution of the nearwall region, which is a critical condition for turbulent modeling, is based on the equations obtained in the nearwall domain decomposition method.

A demonstration of the method is given for the case of a flow around a rounded plate by a turbulent air flow with different rounding at fixed parameters of the incoming flow with the Reynolds number $Re = 10^5$ and the Mach number $M = 0.15$. Since flows with such parameters of the incoming flow can be considered incompressible, only the velocity components are studied directly. The flow fields, velocity and friction profiles obtained by the surrogate model and numerically are compared. The analysis is carried out both on the plate and on the rounding. The simulation results confirm the prospects of the proposed approach. In particular, it was shown that even if the model is used at the maximum permissible limits of its applicability, friction can be obtained with an accuracy of up to 90%. The work also analyzes the constructed architecture of the neural network. The obtained surrogate model is compared with alternative models based on a variational autoencoder or the principal component analysis using radial basis functions. Based on this comparison, the advantages of the proposed method are demonstrated.

The paper considers the problems of mathematical description of deontological aspects influencing the behavior of decision makers. A methodology is proposed for correlating utilitarian (material) and deontological (value) aspects in their decision-making, taking into account their psychological characteristics. A mathematical model is proposed for the joint consideration of utilitarian and deontological factors in decision-making in various situations. Some patterns related to this consideration are identified, and their formal description is given. The model shows that there is a tendency for a gradual decrease in the level of deontology in evaluating alternatives when making decisions (compared to what the outside world inclines to) towards greater utilitarianism. Over time, this trend begins to influence public opinion and society’s attitude to moral norms, gradually reducing the overall level of morality in society. This process can be stopped only by constantly and purposefully maintaining a high level of deontology by society and the state (ideological work, promotion of traditional values, educational work at school, etc.), otherwise society will inevitably become utilitarian over time, focusing exclusively on material factors when making decisions.

In the future, it is planned to use the developed tools for analyzing specific situations, including for analyzing the patterns of civilizational cycles: the rise and fall of the Roman Empire, the USSR, and modern Western civilization).

It proposes a new alternative approach to explain the flat spectrum of the velocity for stars orbital motion on the periphery of spiral galaxies. In particular, that velocity significant excess of speed calculated according to the virial theorem. The concept is the assumption of the existence for gravitational field of the Central body of the galaxy cylindrical, and not spherical, symmetry. The configuration of this field can be explained by the presence on galaxy axis the cosmic string, the length of which covers the diameter of the disk of the galaxy. This model will be subjected to comparison with the more traditional concept of the availability of the spiral galaxy ball halo of dark matter. For this approach it will also be offered a kinematic model, and the hypothesis about the nature of dark matter. It examines the data of astronomical observations about the presence of cosmic strings in the zones adjacent to galaxies.

A simplified implicit Euler method was analyzed as an alternative to the explicit Euler method, which is a commonly used method in numerical modeling in electrophysiology. The majority of electrophysiological models are quite stiff, since the dynamics they describe includes a wide spectrum of time scales: a fast depolarization, that lasts milliseconds, precedes a considerably slow repolarization, with both being the fractions of the action potential observed in excitable cells. In this work we estimate stiffness by a formula that does not require calculation of eigenvalues of the Jacobian matrix of the studied ODEs. The efficiency of the numerical methods was compared on the case of typical representatives of detailed and conceptual type models of excitable cells: Hodgkin–Huxley model of a neuron and Aliev–Panfilov model of a cardiomyocyte. The comparison of the efficiency of the numerical methods was carried out via norms that were widely used in biomedical applications. The stiffness ratio’s impact on the speedup of simplified implicit method was studied: a real gain in speed was obtained for the Hodgkin–Huxley model. The benefits of the usage of simple and high-order methods for electrophysiological models are discussed along with the discussion of one method’s stability issues. The reasons for using simplified instead of high-order methods during practical simulations were discussed in the corresponding section. We calculated higher order derivatives of the solutions of Hodgkin-Huxley model with various stiffness ratios; their maximum absolute values appeared to be quite large. A numerical method’s approximation constant’s formula contains the latter and hence ruins the effect of the other term (a small factor which depends on the order of approximation). This leads to the large value of global error. We committed a qualitative stability analysis of the explicit Euler method and were able to estimate the model’s parameters influence on the border of the region of absolute stability. The latter is used when setting the value of the timestep for simulations a priori.

In the first part of the paper we present convergence analysis of AGMsDR method on a new class of functions — in general non-convex with $M$-Lipschitz-continuous gradients that satisfy Polyak – Lojasiewicz condition. Method does not need the value of $\mu^{PL}>0$ in the condition and converges linearly with a scale factor $\left(1 - \frac{\mu^{PL}}{M}\right)$. It was previously proved that method converges as $O\left(\frac1{k^2}\right)$ if a function is convex and has $M$-Lipschitz-continuous gradient and converges linearly with a~scale factor $\left(1 - \sqrt{\frac{\mu^{SC}}{M}}\right)$ if the value of strong convexity parameter $\mu^{SC}>0$ is known. The novelty is that one can save linear convergence if $\frac{\mu^{PL}}{\mu^{SC}}$ is not known, but without square root in the scale factor.

The second part presents modification of AGMsDR method for solving problems that allow alternating minimization (Alternating AGMsDR). The similar results are proved.

As the result, we present adaptive accelerated methods that converge as $O\left(\min\left\lbrace\frac{M}{k^2},\,\left(1-{\frac{\mu^{PL}}{M}}\right)^{(k-1)}\right\rbrace\right)$ on a class of convex functions with $M$-Lipschitz-continuous gradient that satisfy Polyak – Lojasiewicz condition. Algorithms do not need values of $M$ and $\mu^{PL}$. If Polyak – Lojasiewicz condition does not hold, the convergence is $O\left(\frac1{k^2}\right)$, but no tuning needed.

We also consider the adaptive catalyst envelope of non-accelerated gradient methods. The envelope allows acceleration up to $O\left(\frac1{k^2}\right)$. We present numerical comparison of non-accelerated adaptive gradient descent which is accelerated using adaptive catalyst envelope with AGMsDR, Alternating AGMsDR, APDAGD (Adaptive Primal-Dual Accelerated Gradient Descent) and Sinkhorn's algorithm on the problem dual to the optimal transport problem.

Conducted experiments show faster convergence of alternating AGMsDR in comparison with described catalyst approach and AGMsDR, despite the same asymptotic rate $O\left(\frac1{k^2}\right)$. Such behavior can be explained by linear convergence of AGMsDR method and was tested on quadratic functions. Alternating AGMsDR demonstrated better performance in comparison with AGMsDR.