Computer modeling is designed to predict the behavior of complex systems by solving the corresponding mathematical equations of the physical process. The simulation requires a huge …
In this paper, we use the method of stochastization of one-step processes to demonstrate the advantages of a stochastic representation of the system. To achieve this goal, the …
The purpose of this work is to study the capabilities of the Julia language for numerical modeling of stochastic systems. As a stochastic system we consider the model of …
Recently, the place of the main programming language for scientific and engineering computations has been little by little taken by Julia. Some users want to work completely within …
In research problems that involve the use of numerical methods for solving systems of ordinary differential equations (ODEs), it is often required to select the most efficient …
This work is devoted to studying the possibilities of hybrid modeling of communication network algorithms in the Julia programming language. A simulation of the system, which …
The history of using machine learning algorithms to analyze statistical models is quite long. The development of computer technology has given these algorithms a new breath. …
It is known that modeling consists of a few stages. Analytic calculations are made first and afterwards, they are converted into numerical ones. It is a fairly complicated task, …
Different kinds of models are used to study various natural and technical phenomena. Usually, the researcher is limited to using a certain kind of model approach, not using others …
In this paper the computer paralinguistic model for emotions recognition based on deep neural networks is proposed. The main stages of its construction were studied and relevant …
Imbalance of the classes, characterized by a disproportional ratio of observations in each class, is one of the significant problems in machine learning. Class imbalances can be …
This work is devoted to the study of the capabilities of the Modelica and Julia programming languages for the implementation of a continuously discrete paradigm in modeling hybrid …
In problems of mathematical physics in order to study the structures ofspaces by using the Cayley–Klein models in theoretical calculations, the generalized complex numbers are …
The paper investigates the problem of recognizing human emotions by voice using deep learning methods. Deep convolutional neural networks and recurrent neural networks with …
According to the modest opinion of the authors, data science, machine learning, neural networks are extremely relevant areas of science. Around them there was an expectation that …
The Maxwell equations have a fairly simple form. However, finding solutions of Maxwell's equations is an extremely difficult task. Therefore, various simplifying approaches are …
When the Maxwell equations are geometrized, the Maxwell Lagrangian is usually reduced to the Yang-Mills Lagrangian. In this case, the effective quadratic metric, usually …
The authors for a long time have investigated the phenomenon of global synchronization. Specifically, the occur- rence of a self-oscillatory regime in a control system was …
The most common physical formalisms are the Lagrangian formalism and the Hamiltonian formalism. From the superficial point of view, they are one and the same, but rewritten in …