Computational Mathematics
Description: The course covers numerical methods for solving linear and nonlinear equations, systems of linear algebraic equations, problems of interpolation, numerical differentiation and integration, as well as numerical methods for solving problems in mathematical physics. Special attention is paid to the analysis of stability, convergence, and estimation of computational errors. Practical tasks are provided using programming languages and mathematical packages (for example, Python, MATLAB).
Amount of credits: 6
Course Workload:
Types of classes | hours |
---|---|
Lectures | 15 |
Practical works | 15 |
Laboratory works | 30 |
SAWTG (Student Autonomous Work under Teacher Guidance) | 30 |
SAW (Student autonomous work) | 90 |
Form of final control | Exam |
Final assessment method |
Component: University component
Cycle: Base disciplines
Goal
- Formation of students' theoretical foundations and practical skills in applying numerical methods to solve mathematical problems arising in engineering and scientific practice using modern computing tools
Learning outcome: knowledge and understanding
- know the basic numerical methods and their field of applicability;
Learning outcome: applying knowledge and understanding
- be able to implement numerical algorithms and analyze the results;
- possess the skills of software implementation of mathematical models;
- apply numerical methods in solving engineering and applied problems.
Learning outcome: formation of judgments
- be able to justify the choice of a numerical method for a specific task;
- evaluate numerical methods in terms of their accuracy, robustness, and computational efficiency.
Learning outcome: communicative abilities
- the ability to work in a team in the process of solving practical problems, to express and correctly defend one's point of view in controversial issues.
- четко и логично излагать результаты численных расчетов, представлять их в форме отчетов и презентаций, а также аргументированно обсуждать выбор методов с коллегами
Learning outcome: learning skills or learning abilities
- He is able to independently master new computational methods and software tools, analyze modern literature and documentation on numerical algorithms.