Dmitri Koroliouk – författare

This book is devoted to the system analysis of statistical experiments, determined by the averaged sums of sampling random variables. The dynamics of statistical experiments are given by difference stochastic equations with a speci?ed regression function of increments – linear or nonlinear. The statistical experiments are studied by the sample volume increasing (N ??), as well as in discrete-continuous time by the number of stages increasing (k ??) for different conditions imposed on the regression function of increments. The proofs of limit theorems employ modern methods for the operator and martingale characterization of Markov processes, including singular perturbation methods. Furthermore, they justify the representation of a stationary Gaussian statistical experiment with the Markov property, as a stochastic difference equation solution, applying the theorem of normal correlation. The statistical hypotheses verification problem is formulated in the classification of evolutionary processes, which determine the dynamics of the predictable component. The method of stochastic approximation is used for classifying statistical experiments.

Within the field of modeling complex objects in natural sciences, which considers systems that consist of a large number of interacting parts, a good tool for analyzing and fitting models is the theory of random evolutionary systems, considering their asymptotic properties and large deviations. In Random Evolutionary Systems we consider these systems in terms of the operators that appear in the schemes of their diffusion and the Poisson approximation. Such an approach allows us to obtain a number of limit theorems and asymptotic expansions of processes that model complex stochastic systems, both those that are autonomous and those dependent on an external random environment. In this case, various possibilities of scaling processes and their time parameters are used to obtain different limit results.

This book illustrates a number of asymptotic and analytic approaches applied for the study of random evolutionary systems, and considers typical problems for specific examples. In this case, constructive mathematical models of natural processes are used, which more realistically describe the trajectories of diffusion-type processes, rather than those of the Wiener process.We examine models where particles have some free distance between two consecutive collisions. At the same time, we investigate two cases: the Markov evolutionary system, where the time during which the particle moves towards some direction is distributed exponentially with intensity parameter λ; and the semi-Markov evolutionary system, with arbitrary distribution of the switching process. Thus, the models investigated here describe the motion of particles with a finite speed and the proposed random evolutionary process with characteristics of a natural physical process: free run and finite propagation speed. In the proposed models, the number of possible directions of evolution can be finite or infinite.

Mathematical methods in engineering are characterized by a wide range of techniques for approaching various problems. Moreover, completely different analysis techniques can be applied to the same problem, which is justified by the difference in specific applications. Therefore, the study of the analyses and solutions of specific problems leads the researcher to generate their own techniques for the analysis of similar problems continuously arising in the process of technical development.Computational Methods and Mathematical Modeling in Cyberphysics and Engineering Applications contains solutions to specific problems in current areas of computational engineering and cyberphysics.

Situated at the intersection of advanced applied mathematics, computational science and engineering modeling, Computational Methods and Mathematical Modeling in Cyberphysics and Engineering Applications 2 presents contemporary analytical and numerical approaches to complex multiscale systems. This book develops a coherent framework for modeling coupled processes in porous media, heat and mass transfer, biodegradation-driven consolidation, and micro-irrigation networks, alongside modern operator-theoretic and variational methods in Banach spaces. It integrates finite element and finite difference techniques, quasiconformal mappings for electrical impedance tomography, fractional and biparabolic evolution equations, and homogenization in composite media. This book addresses high-performance and grid computing, geometric integration in Hamiltonian magnetic levitation systems, and hierarchical predictors for lossless image compression. It also explores AI-oriented transformer architectures for bioinformatics and optimization algorithms for variational inequalities. By combining rigorous mathematical foundations with scalable computational strategies, this book offers researchers and advanced practitioners a unified perspective on simulation, identification, and optimization in complex engineering and physical systems.

Responding to the growing demand for rigorous modeling frameworks capable of addressing complex technological challenges across disciplines, this book offers a coherent panorama of contemporary methodologies. These span lossless image compression algorithms, electrical impedance tomography and extragradient methods for variational inequalities in Banach spaces. It further examines multiphysics modeling in environmental engineering, grid-supported magnetic levitation systems and the optimization of water transport in porous media for sustainable irrigation. Foundational stochastic tools – such as gamma processes and hidden Markov models for reliability and maintenance planning – are integrated with theoretical advances, including matrix polynomials on noncommutative sets.