Implementation of artificial intelligence development strategy in the Russian Federation in the educational programs of aerospace engineering training of Bauman Moscow State Technical University

. As a part of the National Strategy on Artiﬁcial Intelligence development in the Russian Federation, the speciﬁc features of training in USSN 24.00.00 Aviation and Rocket-Space Engineering at “Mechanical Engi-neering” (SM) Department of Bauman Moscow State Technical University were considered. The analysis of self-established educational standards and the clas-siﬁcation of artiﬁcial intelligence course modules, implemented at the SM Department, is carried out. Individual courses that already use methods of artiﬁcial intelligence or are promising for the introduction of such methods are presented.


Introduction
Artificial Intelligence (AI) is a set of technological solutions that allows simulating human cognitive functions (including self-learning and searching for solutions without a predetermined algorithm) and obtaining results comparable at least to the results of human intellectual activity when performing specific tasks.
Although the first information systems for assisting people in making decisions date back to the mid-1950s, recent years have seen rapid growth in methods, algorithms, and practical applications of AI in various fields of activity.
Presidential Decree No. 490 of October 10, 2019 "On the Development of Artificial Intelligence in the Russian Federation" and the adopted accordingly National Strategy for the Development of Artificial Intelligence to the year 2030 (https://www.garant.ru/products/ipo/prime/doc/72738946/) determines both general tasks of the accelerated development of artificial intelligence in the Russian Federation and the major directions for enhancement of training system in this field.That includes conducting scientific research in the field of artificial intelligence and increasing the availability of information and computing resources for users.
At the Artificial Intelligence Journey (AI Journey 2020) conference on "Artificial Intelligence-Main Technology for the 21 st Century" (http://www.kremlin.ru/events/president/news/64545), the goal for education was specified in a Presidential address: (quote) "I believe that training courses, modules on artificial intelligence and big data analysis should be included in educational programs, and for all areas of professional activity".
Following the conference on Artificial Intelligence, the President approved the list of instructions, particularly according to Pr-2242, paragraph 1 e) -2: to complement the educational programs of higher education in all fields of specialization and areas of training with sections on the study of artificial intelligence for training in the application of such technologies in various fields of activity.
According to clause 45 of the National Strategy, the main directions of increasing the amount of qualified personnel for the Russian market of artificial intelligence technologies and the level of public awareness about the possible areas of use of such technologies are, inter alia: Paragraph a)-development and implementation of educational modules as part of educational programs at all levels of education, advanced training, and professional retraining programs to provide citizens with knowledge, competencies, and skills in mathematics, programming, data analysis, machine learning, contributing to the development of artificial intelligence.
Herewith, the convergent knowledge provided through integration of mathematical, natural science, and social and humanities education is a priority for the development of promising methods of artificial intelligence.
Paragraph c)-improving the quality of mathematical and natural science education of students (within both primary and complementary educational programs), its integration with social and humanities education, creating conditions for attracting students to advanced training in these areas.
This article considers both the current state of training specialists for USSN (consolidated group of training areas and majors) 24.00.00Aviation and Rocket-Space Engineering at the "Mechanical Engineering" (SM) Department of Bauman Moscow State Technical University, and the vectors of implementation of artificial intelligence development strategy in the curriculum for this program.
The promising methods of artificial intelligence focused on the creation of fundamentally new scientific and technical products, have been analyzed in advance.They include: • autonomous task solving; • automatic design of physical objects; • automatic machine learning; • information processing based on new types of computing systems; • interpretive data processing; • big data processing; • intelligent data analysis; • intelligent decision support.
The experience of training specialists in the field of aerospace technology is discussed in articles [1,3].The works [4,10] are devoted to the problems and methods of AI, and the peculiarities of intelligent systems, as well as their application in the aerospace sphere.
The problem of including one or another AI technology in the educational process depends on the training program.The basis in the development of core professional educational programs (CPEP) for this major at Bauman Moscow State Technical University is the independently established educational standards (IEES). of Rocket and Space Engineering" (24.05.01 program, Design, Production and Exploitation of Aerospace Systems); SM-3 "Orbital Mechanics and Flight Control" (24.05.04 program, Navigation and Ballistic Support of Space Applications); SM-5 "Autonomous Information and Control Systems"; SM-13 "Aerospace Composite Structures" (undergraduate and graduate training programs 24.03.01,24.04.01,Rocket Systems and Cosmonautics).
Primarily, it is necessary to analyze the IEEE in terms of the areas of professional activity that they contain: • 25 Rocket and Aerospace Industry (fields: engineering and technology related to design, testing, operation and effectiveness assessment of aerospace and rocket hardware, barrel and jet artillery, study of ballistic, aerodynamic and kinematic properties and features of professional objects, navigation and ballistic support of their application, dynamics and control of motion (flight), space flight control, ballistic design); • 32 Aviation (fields: aerodynamic design of promising models of aviation and aerospace technology, ground and flight aerodynamic testing of models, mock-ups and full-scale structures of manned and unmanned aircraft).
All promising methods of artificial intelligence should be included in the department's training programs.
An analysis of competencies embedded in the IEES implemented in the SM department was also carried out.In particular, the study of AI methods will contribute to the achievement of the following competencies: • Fundamental Universal Competences (UCF): UCF-1 "Is capable of search, critical analysis and synthesis of information, its semantic optimization and visual presentation, apply a systematic approach to solve the tasks; use the basics of philosophical knowledge and analyze the patterns of the historical development of society to form worldview and civic position"; • Fundamental General Professional Competencies (GPCF): GPCF-1 "Is capable of applying natural science and general engineering knowledge, methods of mathematical analysis and modeling, theoretical and experimental research skills to solve various professional tasks"; GPCF-2 "Is capable of understanding the principles of modern information technologies and use them for analyzing and solving professional tasks"; GPCF-6 "Can develop physical and mathematical models of space and rocket-to-satellite objects"; GPCF-9 "Is capable to use scientific and creative skills to search and find non-trivial solutions of scientific and technical problems and create innovations in the rocket and space equipment design field"; • Fundamental Professional Competencies (PCF): PCF-1 "Is capable of forming the logic of spacecraft control system functioning in case of on-board equipment failures"; PCF-4 "Is capable of conducting research work, coordinating and managing the processes of developing the design and technological documentation at all stages of the aerospace product life cycle"; PCF-6 "Is capable of conducting scientific analysis and forecasting the requirements development for the GNSS means and tools and their functional additions".The detailed analysis of Artificial Intelligence methods leads to the following classification of artificial intelligence course modules, already implemented in the SM department: • "Artificial Intelligence Methods (General Course)" (7 courses); • "Fundamentals of Data Analysis" (12 courses); • "Data Analysis and Machine Learning" (10 courses); • "Neural Networks" (5 courses); • "Big Data Processing Systems" (3 courses); • "Knowledge Engineering" (6 courses).
In each sub-department, among the studied programs, we found courses that can serve as a basis for the organization of intra-and interuniversity interaction for the preparation of new and modification of existing artificial intelligence curriculum.
Table 1 shows the most interesting topics of individual disciplines that already use methods of artificial intelligence or are promising for the introduction of such methods.

Conclusions
1. Sub-departments are divided into two groups: • ready-made courses in AI methods; • aspects of the application of AI methods in courses.

3 Analysis of the implemented curricula and course programs of the SM Department for specialists in USSN 24.00.00 where methods, technologies, and examples of the application of AI are covered
The course programs of sub-departments at the Mechanical Engineering Department were analyzed.All students of the Mechanical Engineering Department in USSN 24.00.00 study in some form individual methods, modules, or full courses of Artificial Intelligence.

Table 1 .
Disciplines topics of the SM Departments at USSN 24.00.00 in which AI methods are to be studied and/or implemented Discrete adaptive systems with an implicit reference model.Discrete adaptive systems with a configurable model of the control object.Statistical methods of identification.Regression analysis.Identification of a nonlinear system.Hammerstein's model.Measurement errors.Measurement function errors.Model errors.Accuracy assessment.

Table 1 .
(Continue) Development of tests for students in the MS Forms environment with scoring in an automated mode based on the completed survey forms.Implementation of AI in the educational process).

Table 1 .
(Continue) Automation of the design process in the Ansys environment using the Fortran/APDL programming language.The application of multilayer perceptron from the Python Sklearn library for constructing dependencies between input and output parameters of technical objects.Fundamentals of implementation of parallel computations in gas dynamics, heat transfer, and strength problems.The structure of modern computer-aided design systems.Statistical processing of experimental data.Testing statistical hypotheses, the choice of data distribution law.Application of planning algorithms for multifactor experiments (Python DOE library).Application of the high-level Pandas Python library for big data analysis.Series and Dataframe data structures.