SylabUZ
| Course name | Numerical methods in engineering |
| Course ID | 11.9-WE-ELEKTD-NumMethinTechn-Er |
| Faculty | Faculty of Computer Science, Electrical Engineering and Automatics |
| Field of study | Electrical Engineering |
| Education profile | academic |
| Level of studies | Second-cycle Erasmus programme |
| Beginning semester | winter term 2017/2018 |
| Semester | 1 |
| ECTS credits to win | 5 |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
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| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Lecture | 15 | 1 | - | - | Exam |
| Laboratory | 15 | 1 | - | - | Credit with grade |
- familiarize students with the basic numerical methods properties that are used for engineering calculations
- formation among the students of understanding the need for correct implementation of computer calculations with acceptable errors
- basic ability formation of numerical methods for practical use in computer calculations – using Matlab
Selected issues of circuit theory I and II
Mathematical bases. Bases conception and theorems of mathematical analyse used in numerical methods, Taylor’s series.
Errors and representation of numbers. Bases definitions and type of errors, badly conditional systems, numerical stability, methods to avoid errors, decimal system, binary system, sexadecimal system, floating point numbers, fixed-point numbers, coupling with errors
Finding roots of nonlinear equations. Methods: bisection method, Newton’s method, secant method, Banach fixed point method use, analyse and errors estimation, extrapolation, case of badly conditional system, numerical stability of solutions
Interpolation. Interpolation characterization and its using. Lagrange’s formula, residual quotients, property and Newton’s formula.
Error analyses: spline Interpolation, Hermite’s interpolation. Approximation. Least square method, mean squared error, orthogonal polynomial using. Numerical integration, Newtona-Coatesa’s quadrature – trapezium method, Gauss’s quadrature, analyses and errors estimation, Richardson’s extrapolation.
Lecture, laboratory exercises
| Outcome description | Outcome symbols | Methods of verification | The class form |
Lecture – obtaining a positive grade in written exam.
Laboratory – the main condition to get a pass are sufficient marks for all exercises and tests
conducted during the semester.
Calculation of the final grade: lecture 50% + laboratory 50%
1. Baron B.: Metody numeryczne, Helion, Gliwice, 1995.
2. Fortuna Z., Macukov B., Wąsowski J.: Metody numeryczne, WNT, Warszawa, 1982.
3. Klamka J. i inni: Metody numeryczne, Oficyna Wydawnicza Politechniki Śląskiej, Gliwice, 1998.
1. Bjoerck A., Dahlquist G.: Metody numeryczne, PWN, Warszawa, 1987.
Modified by dr hab. inż. Radosław Kłosiński, prof. UZ (last modification: 27-04-2017 10:47)
