Computational Science

This discipline covers an introduction to mathematical courses necessary for mastering specialized disciplines of computational science based on numerical solutions of deterministic and probabilistic equations of mathematical physics and applied models used in technical production and the financial sector. Namely, it covers the theory of ordinary differential equations, their typification and basic methods of analytical solution and an introduction to partial differential equations.

This course explores innovative pedagogical approaches and effective techniques for engaging and facilitating learning in diverse educational settings.

This discipline involves the study of the fundamentals of numerical methods in the field of modeling physical processes, including algebraic numerical methods, numerical integration and numerical solution of partial differential equations, it also studies the introduction to methods of finite differences.

This discipline focuses on common mathematical models used in manufacturing and their solution using numerical methods. Upon mastering the discipline, the student must know: basic mathematical models such as “predator-prey”, the equation of heat conduction, etc .; basic models of hydrodynamics, filtration, chemical reactions; be able to: approximate and investigate the convergence of the model; apply basic numerical methods to solve applied problems.

Research practice

Case study in computational sciences

This discipline is devoted to methods of constructing unstructured and structured grids that adapt to certain properties of the area and their use for solving numerical problems in these areas. Such methods of structured grids as methods of equidistribution, Thompson”s method, and such methods of unstructured grids as Delaunay triangulation, Voronoi diagram are considered.

Physics Inferred Neural Networks

This course introduces students to various generative modeling techniques and algorithms, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and autoregressive models. Students will learn how to design, train, and evaluate generative models, as well as explore their applications in fields like computer vision, natural language processing, and data synthesis.

The course is designed to study the management of stakeholders (stakeholders) of the project. Undergraduates will consider the basic principles and analysis of the external and internal environment of the project, aimed at identifying and systematizing the main stakeholders, assessing their goals, collecting information about them and using this data in the project management process. It will also consider negotiating and engaging stakeholders to collaborate with managing the expectations of key stakeholders.

Discipline that focuses on the strategic planning, development, and successful execution of products throughout their lifecycle. It includes a wide range of activities and responsibilities aimed at creating valuable and marketable products that meet customer needs and meet business goals.

The course covers the area of the service approach in organizing the company’s activities; service tools and services provided by internal divisions and/or external contractors

This course is a concept, languages, methods and patterns for programming heterogeneous, massive parallel processors. It covers heterogeneous computing architectures, software programming models, memory launching methods and parallel algorithms on the example of CUDA and OpenCl.

This course will introduce students to the basics of reinforcement learning. Upon completion of this course, the student will be able to: Formalize problems as Markov decision-making processes; Understand basic exploration techniques and trade-offs between exploration and exploitation; Understand value functions as a universal tool for making optimal decisions; Know how to implement dynamic programming as an effective approach to solving industrial control problems.

This discipline involves the study of quantum computing methods and their advantages over classical computation methods. The course covers the main provisions of the classical theory of computational complexity, the gate model of quantum computing, universal sets of gates, quantum computing algorithms based on the quantum Fourier transform, in particular, Shor’s algorithm, quantum search algorithms, algorithms for quantum simulation of physical systems, an introduction to quantum error correction, and error-resistant computations, hybrid quantum-classical algorithms, in particular, variational quantum algorithms.