Subject Datasheet

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I. Subject Specification

1. Basic Data
1.1 Title
Numerical Methods
1.2 Code
BMEEOAFMSFAL01-00
1.3 Type
Module with associated contact hours
1.4 Contact hours
Type Hours/week / (days)
Lab 3
1.5 Evaluation
Midterm grade
1.6 Credits
4
1.7 Coordinator
name Dr. Laky Piroska
academic rank Associate professor
email laky.piroska@emk.bme.hu
1.8 Department
Department of Geodesy and Surveying
1.9 Website
1.10 Language of instruction
hungarian
1.11 Curriculum requirements
Compulsory in the Structural Engineering (MSc) programme
Compulsory in the Infrastructure Engineering (MSc) programme
Compulsory in the Land Surveying and Geoinformatics (MSc) programme
Compulsory in the Construction Information Technology Engineering (MSc) programme
1.12 Prerequisites
1.13 Effective date
1 September 2025

2. Objectives and learning outcomes
2.1 Objectives
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 Learning outcomes
Upon successful completion of this subject, the student:
A. Knowledge
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. Skills
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. Attitudes
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. Autonomy and Responsibility
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 Methods
- Lectures, computer laboratory practices and consultations. - Independent exercises to be done at home.
2.4 Course outline
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
The above programme is tentative and subject to changes due to calendar variations and other reasons specific to the actual semester. Consult the effective detailed course schedule of the course on the subject website.
2.5 Study materials
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 Other information
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 Consultation
As specified on the department’s website, or in consultation with the course instructors via e-mail: laky.piroska@emk.bme.hu
This Subject Datasheet is valid for:
2025/2026 semester II

II. Subject requirements

Assessment and evaluation of the learning outcomes
3.1 General rules
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 Assessment methods
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

The dates of deadlines of assignments/homework can be found in the detailed course schedule on the subject’s website.
3.3 Evaluation system
CodeWeight
MT135%
MT235%
P30%
Total100%
3.4 Requirements and validity of signature
Signature could not be obtained from the subject.
3.5 Grading system
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 Retake and repeat
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 Estimated workload
ActivityHours/Semester
Contact hours14×3=42
Midterm preparation for laboratory practices14x1=14
Preparation for the tests2×22=44
Practice exercises20
3.8 Effective date
1 September 2025
This Subject Datasheet is valid for:
2025/2026 semester II