Course detail

Mathematics 5 (M)

FAST-NAA021Acad. year: 2024/2025

Introduction to numerical mathematics, namely interpolation and approximations of functions, numerical differentiation and quadrature, analysis of algebraic and differential equations and their systems.

Language of instruction

Czech

Number of ECTS credits

4

Mode of study

Not applicable.

Guarantor

prof. Ing. Jiří Vala, CSc.

Department

Institute of Mathematics and Descriptive Geometry (MAT)

Entry knowledge

Basic courses of mathematics for bachelor students, MATLAB programming (as in the recommended course at MAT FCE).

Rules for evaluation and completion of the course

Extent and forms are specified by guarantor’s regulation updated for every academic year.

Aims

Students will obtain the basic knowledge of numerical mathematics, probability and statistics, applied to technical problems, especially from material engineering.
Following the aim of the course, students will receive the basic orientaion in numerical and statistical methods needed in material engineering and in related engineering applications.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

DALÍK J.: Numerické metody. CERM Brno 1997. (CS)

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme NPC-SIM Master's 1 year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Mgr. Ing. Miroslav Trcala, Ph.D.

Syllabus

1. Errors in numerical computations. Contractive mappings, application to solution of nonlinear algebraic equations: simple iterative method, Newton method, method of secants. 2. Direct methods for solution of systems of linear algebraic equations, namely multiplicative decompositions: LU decomposition, Choleski decomposition, idea of QR decomposition. 3. Iterative and relaxation methods for solution of systems of linear algebraic equations, namely Jacobi and Gauss-Seidel methods including relaxation. 4. Conjugate gradient method, namely for systems of linear algebraic equations. Newton method for nonlinear systems. 5. Conditionality of systems of equations. Least squares method: idea, discrete case. 6. Lagrange interpolating polynomial, namely Newton form. Hermite interpolating polynomial. 7. Cubic splines: idea for Lagrange splines, calculations for Hermite splines. 8. Numerical differentiation. Finite difference method, application to boundary value problems for ordinary differential equations of order 2. 9. Numerical integration: rectangular, trapezoidal and Simpson rule, including approximation error estimate. Idea of more-dimensional numerical integration. 10. Finite element method, application to boundary value problems for ordinary differential equations of order 2. 11. Time-dependent problems: Euler explicit and implicit method, Crank-Nicolson method and Runge-Kutta methods, application to initial value problems for ordinary differential equations of order 1. 12. Continuation and completion of preceding themes, comments to engineering applications. 13. Finite element method for partial differential equations, example of equation of heat transfer.

Exercise

13 hod., compulsory

Teacher / Lecturer

prof. Ing. Jiří Vala, CSc.
doc. Ing. Vladislav Kozák, CSc.

Syllabus

1.-2. Introduction to MATLAB: MATLAB environment, MATLAB online, assignment to variables, double dot, operations with number and vectors, plot, comments, MATLAB help, cycle for-end and condition if-else-end. Setting individual semester work. 3.-4. Repetition of methods for solution of 1 nonlinear equation: function graph and root estimate, script for 1 specific example and method of bisection, generalization for an arbitrary functions and initial inputs (for, if, plot, anonymous function). 5.-7. Implementation of iterative methods for solution of systems of linear algebraic equations: matrix operations (*, .*, +, inv, det, size and similar), vector norm, creation of solver with a lower triangular matrix, consequently creation of script for Gauss-Seidel method in matrix notation, creation of a function including check of inputs (diagonal dominance, etc.). 8.-9. Approximation of functions: least squares method in matrix form, usage of prepared Gauss-Seidel iteration for solution of a normal equation, Lagrange interpolation – form of a polynomial and setting coefficients, possible relation to numerical integration following composed rectangular rule. 11.-12. Ordinary differential equations: explicit and implicit Euler method for order 1, finite difference method for order 2, utilization of prepared solver of systems of linear algebraic equations, comparison with finite element method. 13. Evaluation of semester work.