Subject Datasheet

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I. Tantárgyleírás

1. Alapadatok
1.1 Tantárgy neve
Numerical Methods
1.2 Azonosító (tantárgykód)
BMEEOAFMSFAL01-00
1.3 Tantárgy jellege
Kontaktórás tanegység
1.4 Óraszámok
Típus Óraszám / (nap)
Laboratóriumi gyakorlat 3
1.5 Tanulmányi teljesítményértékelés (minőségi értékelés) típusa
Félévközi érdemjegy
1.6 Kreditszám
4
1.7 Tárgyfelelős
név Dr. Laky Piroska
beosztás Egyetemi docens
email laky.piroska@emk.bme.hu
1.8 Tantárgyat gondozó oktatási szervezeti egység
Általános- és Felsőgeodézia Tanszék
1.9 A tantárgy weblapja
1.10 Az oktatás nyelve
magyar
1.11 Tantárgy típusa
Kötelező a Szerkezet-építőmérnök (MSc) szakon
Kötelező az Infrastruktúra-építőmérnök (MSc) szakon
Kötelező a Földmérő- és térinformatikai mérnök (MSc) szakon
Kötelező az Építményinformatikai mérnök (MSc) szakon
1.12 Előkövetelmények
1.13 Tantárgyleírás érvényessége
2025. szeptember 1.

2. Célkitűzések és tanulási eredmények
2.1 Célkitűzések
The aim of this course is for students to learn and apply the possibilities of numerical solutions to engineering problems on computers at a good skill level. The principles of the most relevant numerical techniques including their advantages, disadvantages, and applicability are presented during laboratory exercises. Students may learn and apply mathematical procedures suitable for solving and visualizing engineering problems on computers, mainly through civil engineering examples. A further aim of the course is to prepare the students for later independent research.
2.2 Tanulási eredmények
A tantárgy sikeres teljesítése utána a hallgató
A. Tudás
1. Has a skill-level understanding of a mathematical environment 2. Is familiar with the basic commands, instructions, loops, branches, graphical display options, text reading, and writing possibilities of a given mathematical environment 3. Can distinguish between different computation errors 4. Knows methods for solving systems of linear equations 5. Understands root-finding procedures for nonlinear systems of equations 6. Is aware of the difference between interpolation and regression methods 7. Has an overview of some optimization methods 8. Is aware of various numerical derivation, and integration procedures 9. Is familiar with some methods for solving initial value and boundary value problems of ordinary differential equations
B. Képesség
1. Able to skillfully use a mathematical environment to solve engineering problems 2. Able to interpret the upcoming error/warning messages and to fix the specified errors. 3. Can use the software documentation effectively, find the necessary commands, and interpret the algorithms and parameters of the commands 4. Able to load text data into a mathematical environment 5. Is proficient in creating graphs in a mathematical environment, parameterizing them as required 6. Selects the most appropriate methods to solve a given problem 7. Is able to fit an interpolation or regression curve/surface to measured data 8. Is proficient in solving linear and non-linear systems of equations 9. Able to solve one or multivariate optimization problems with or without constraints. 10. Able to differentiate/integrate numerically in case of a certain problem 11. Is able to convert a higher-order differential equation into a system of first-order differential equations for the numerical solution 12. Able to solve ordinary differential equations in case of initial or boundary value problem, even in single and bivariate case
C. Attitűd
1. Tries to perform his/her tasks to the best of his/her ability and to a high standard 2. Seeks the most efficient algorithm during the solution 3. Susceptible toward simple and efficient program codes 4. Attempts to write a well-documented script with comments understandable for others 5. Strives for continuous self-learning.
D. Önállóság és felelősség
1. Independently performs the solution of the problem assigned as homework 2. Is open to well-founded critical comments, accepts them and incorporates them into further work 3. Independently checks in the documentation how to use the commands required to solve the tasks 4. Uses cognitive skills to make decisions and to move logically from one idea to another.
2.3 Oktatási módszertan
- Lectures, computer laboratory practices and consultations. - Independent exercises to be done at home.
2.4 Részletes tárgyprogram
1. Introduction to a mathematical environment, writing functions, using logical variables 2. Conditionals and loops, reading data from a file, graphical representations 3. Computational errors 4. Systems of linear equations 5. Systems of non-linear equations 6. Regression 7. Interpolation 8. Summative evaluation 9. Numerical derivation 10. Numerical integration 11. Optimization 12. Ordinary differential equation I. (initial value problem) 13. Ordinary differential equation II. (boundary value problem) 14. Summative evaluation
A félév közbeni munkaszüneti napok miatt a program csak tájékoztató jellegű, a pontos időpontokat a tárgy honlapján elérhető "Részletes féléves ütemterv" tartalmazza.
2.5 Tanulástámogató anyagok
1) Books and online materials: a. Matlab documentation - https://www.mathworks.com/help/matlab/ b. Todd Young and Martin J. Mohlenkamp (2023): Introduction to Numerical Methods and Matlab Programming for Engineers, Department of Mathematics, Ohio University, Dec 13, 2023, (Creative Commons Attribution-Non Commercial-Share Alike 4.0 International License), http://www.ohiouniversityfaculty.com/youngt/IntNumMeth/book.pdf c. Amos Gilat, Vish Subramaniam (2011): Numerical methods, An introduction with Applications Using MATLAB, John Wiley & Sons, ISBN 978-0-470-87374-8, 460 pages 2) Presentations, descriptions, tasks available on the educational framework
2.6 Egyéb tudnivalók
During class work, students are allowed to use their own laptops, provided they have the software used in the exercise, but when writing tests they must use the laboratory computers. During the practical part of the summative assessments, the aids in the teaching framework may be used.
2.7 Konzultációs lehetőségek
As specified on the department’s website, or in consultation with the course instructors via e-mail: laky.piroska@emk.bme.hu
Jelen TAD az alábbi félévre érvényes:
2025/2026 semester II

II. Tárgykövetelmények

3. A tanulmányi teljesítmény ellenőrzése és értékelése
3.1 Általános szabályok
The assessment of the learning outcomes specified in clause 2.2 above and the evaluation of student performance occurs via two midterm tests and practical diagnostic assignment tasks. A minimum of 70% participation in the laboratory exercise is required for successful completion of the semester.
3.2 Teljesítményértékelési módszerek
Assessment Name (Type) Code Assessed Learning Outcomes
1. Midterm test (Summative assessment) MT1 A.1-A.6; B.1-B.8; C.1-C.5; D.1-D.4
2. Midterm test (Summative assessment) MT2 A.6-A.9; B.1-B.12; C.1-C.5; D.1-D.4
Practice exercises (Diagnostic assessment tasks) P A.1-A.9; B.1-B.12; C.1-C.5; D.1-D.4

A szorgalmi időszakban tartott értékelések pontos idejét, a házi feladatok ki- és beadási határidejét a "Részletes féléves ütemterv" tartalmazza, mely elérhető a tárgy honlapján.
3.3 Teljesítményértékelések részaránya a minősítésben
CodeWeight
MT135%
MT235%
P30%
Total100%
3.4 Az aláírás megszerzésének feltétele, az aláírás érvényessége
Signature could not be obtained from the subject.
3.5 Érdemjegy megállapítása
GradeScore (P)
excellent (5)86≤P
good (4)73≤P<86%
satisfactory (3)60≤P<73%
pass (2)50≤P<60%
fail (1)P<50%
3.6 Javítás és pótlás
Both midterm tests have a retake possibility. The actual dates of the retakes can be found in the „Detailed course schedule” on the course website. The result of the last test will be the final result for each test.
3.7 A tantárgy elvégzéséhez szükséges tanulmányi munka
ActivityHours/Semester
Contact hours14×3=42
Midterm preparation for laboratory practices14x1=14
Preparation for the tests2×22=44
Practice exercises20
3.8 A tárgykövetelmények érvényessége
2025. szeptember 1.
Jelen TAD az alábbi félévre érvényes:
2025/2026 semester II