(in Polish) Komputerowe metody obliczeniowe 390-FS1-2KMO

Study profile: general academic

Form of studies: full-time

Module: computer science tools in physics

Field and discipline of science: mathematical sciences, computer science

Year of study, semester: 1st year, 2nd semester, first-cycle studies

Prerequisites: ability to use a computer with Windows/Linux, good knowledge of mathematics at least at secondary school level

Number of teaching hours: laboratory (30 hours)

Teaching methods: discussion, practical classes, consultations, independent work at home

ECTS credits: 3

Student workload balance: participation in classes (30 hours, 1 ECTS credit), preparation for classes and assessment (50 hours, 2 ECTS credits); additionally, students are offered the opportunity to participate in consultations (15 hours per semester)

Quantitative indicators: student workload requiring direct teacher contact (30 hours, 1 ECTS credit), student workload not requiring direct teacher contact (50 hours, 2 ECTS credits)

Rules for the use of artificial intelligence (AI):

The use of AI during classes is permitted as a supporting tool (not a substitute for independent work) for explaining difficult issues, analyzing code errors, obtaining optimization suggestions, and seeking inspiration for solutions.
Problem solving should be carried out independently first; AI assistance is allowed only in case of difficulties.
AI-generated responses must always be verified; students are required to check the correctness of generated code and understand its operation.
If AI assistance is used in an assignment or project, it must be clearly indicated in the documentation, e.g. in a code comment: “Generated using AI (tool name) – modified and adapted.”
The use of AI is prohibited for cheating, in particular generating entire assignments, copying ready-made solutions without understanding, violating academic integrity, and during course assessments, which require independent work.

Course program (laboratory):

Introduction to the programming environment
Basic symbolic and numerical calculations
Lists, arrays, vectors, and matrices
Plots and data visualization
Script files
Control structures
Functions
Solving linear equations
Numerical differentiation
Numerical integration
Data approximation
Interpolation
Monte Carlo method

Term 2024:

Educational profile: general academic
Type of course stationary
Module: obligatory
Area and discipline of science: mathematical sciences, computer science
Module: IT tools
Study year/semester: year 1, semester 1
Preliminary requirements: basics of using computer with Windows / Linux, good knowledge of mathematics at the level of at least of secondary school
Number of teaching hour: laboratory 30h
Teaching methods: discussion, practical classes, consultations, student's own work at home
ECTS scores: 3
Total student workload: participation in the laboratory (30 h), participation in consultations (30 h), own work at home and preparation for credit (30 h)
Quantitative ratios: student workload associated with activities requiring direct teacher participation - 1.8 ECTS; student workload associated with practical activities - 1.2 ECTS

Programme of classes:
1. Introduction to Mathematica / Octave
2. Simple calculations
3. Programming environment
4. Lists, tables, vectors and matrices
5. Plots
6. Script files
7. Control statements
8. Functions
9. Linear equations
10. Numerical differentiation
11. Numerical integration
12. Approximation
13. Interpolation
14. Monte-Carlo method

Prerequisites (description)

Before enrolling in Computational Methods, students are expected to have basic knowledge of linear algebra and calculus, including operations on vectors and matrices, derivatives, and integrals. They should be able to apply elementary mathematical concepts to simple computational problems and be familiar with basic computer use and work in a software environment. Prior experience with programming or computational tools is beneficial but not required.

Course coordinators

Marek Brancewicz

Type of course

obligatory courses

Requirements

Analysis I
(in Polish) Narzędzia komputerowe
Intoductory Mathematics

Prerequisites

Algorithms and Data Structures
Numerical Methods
(in Polish) Programowanie
(in Polish) Programowanie II

Mode

Term 2024:

Blended learning
(in Polish) w sali
(in Polish) zdalnie

Term 2025:

Blended learning
(in Polish) w sali
(in Polish) zdalnie

General:

(in Polish) w sali
(in Polish) zdalnie
Blended learning

Learning outcomes

Knowledge — the graduate knows and understands:
KP6_WG4 - knows advanced computational methods used to solve typical physical problems, as well as examples of practical implementation of such methods using appropriate IT tools; knows elements of programming and software engineering within the scope defined by the study curriculum.

Skills — the graduate is able to:
KP6_UW4 - apply numerical methods to solve mathematical problems; has the ability to use basic software packages and selected programming languages within the scope defined by the study curriculum.
KP6_UK5 - perform critical analysis of measurement results, observations, or theoretical calculations, including quantitative assessment of the accuracy of the results.
KP6_UU1 - learn independently.

Social competences — the graduate is ready to:
KP6_KR2 - apply and promote the principles of intellectual honesty in their own activities and in those of others; resolve ethical issues in the context of research integrity; promote the decisive role of experiment in verifying physical theories; apply the scientific method in knowledge acquisition.

Assessment criteria

Laboratory assessment: practical test

During assessments, the use of electronic communication devices and artificial intelligence (AI) tools is prohibited.

Depending on the applicable regulations, the possibility of conducting the final assessment or final examination using electronic communication tools is reserved.

A necessary condition for passing the laboratory is attendance exceeding 50% of classes. The final grade is based on the total number of points obtained from the final test (weight 75%) and homework assignments (weight 25%). The student receives a grade according to the following scale:
<0;50)% – 2.0
<50;60)% – 3.0
<60;70)% – 3.5
<70;80)% – 4.0
<80;90)% – 4.5
<90;100>% – 5.0

Bibliography

[1] R. L. Zimmerman, F. I. Olness, Mathematica for, Addison-Wesley, 1995
[2] W. Kinzel, G. Reents, transl. by M. Clajus and B. Freeland-Clajus, Physics by computer: programming physical problems using Mathematica and C, Berlin, Springer, 1998
[3] 1st Octave on-line manual: https://docs.octave.org/octave.pdf
[4] 2nd Octave on-line manual: http://www-mdp.eng.cam.ac.uk/web/CD/engapps/octave/octavetut.pdf
[5] Z. Fortuna, B. Macukow, J. Wąsowski, Metody numeryczne, Warszawa, Wydawnictwo WNT, 2015

Term 2024:

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 390-FS1-2KMO in USOSweb