Scientific programming and numeric simulations

1.

Subject title

Scientific programming and numeric simulations

Научно програмирање и нумерички симулации

2.

Code

m23_w_043

3.

Study program

Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Eco-informatics, Inteligent Systems, Internet Technologies and cyber security, Statistics and Data Analytics, Software for embedded systems, Software Engineering, Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Statistics and Data Analytics, Data science in computer science and engineering, Еducation with ICT, Computer Science, Software Engineering,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Летен

7. Number of ECTS credits

6.0

8.

Instructor

проф. д-р Боро Јакимовски проф. д-р Горан Велинов виз. проф. д-р Владо Спиридонов

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

Course Objectives (Competences): Introducing in practical application of various techniques in scientific programming. Studying the most common problems in scientific programs and the relevant libraries to solve them. Parallization of existing solutions using models of distributed and shared memory as well as GPGPU. Study of current trends in the distributed (Grid) calculation and ways of exploiting them in scientific programming. Upon completion of the course, the student is expected to be able to implement a parallel or distributed solution to scientific problems using public scientific libraries. At this laboratory course, students simulate the atmosphere using contemporary numerical models and grows to predict time (WRF) and explore the physical and numerical foundation of the equation system and the numerical methods that support numerical models for prediction. Во прилог на развој на технички вештини со WRF и визуелизирање на излезните податоци од моделот со Python, студентите истражуваат апликации на нумеричко моделирање на атмосферата во ограничена област со самостојна подготовка на нумерички експерименти и симулации на атмосферските појави и системи, циклони, урагани, тајфуни, Storms as well as processes related to air pollution, sand transport, volcanic eruptions and so on.

11.

Subject content:

Course content: - parallel processing with shared and distributed memory - Distributed processing - GPGPU processing - sources of errors - Basic numerical analysis - scientific libraries for linear algebra - Nonlinear optimizations - Solving dynamic systems - Monte Carlo Method -Introduction to an advanced atmospheric model -Compiling and configuring the latest version of the model -initiation, initial and limit conditions, data assimilation (pre-proprocessing); -Numeric integration (Processing) -Visualization of Post-Processing; -Verification of the results.

12.

Learning methods:

Предавања поддржани со презентации преку слајдови, интерактивни предавања, вежби (користење на опрема и софтверски пакети), тимска работа, пример случаи, поканети гости предавачи, самостојна изработка и одбрана на проектна задача и семинарска работа, учење во електронско опкружување (форуми, консултации).

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

45 + 15 + 30 + 50 + 40 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

45 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

15 hours

16.

Other forms of activities

16.1.

Project tasks

50 hours

16.2.

Independent tasks

30 hours

16.3.

Homework

40 hours

17.

Grading method

17.1.

Tests

45 points

17.2.

Seminar work / project (presentation: written and oral)

50 points

17.3.

Activities and learning

10 points

17.4.

Final exam

0 points

18.

Grading criteria (points / grade)

up to 50 points

5 (five) (F)

from 51 to 60 points

6 (six) (E)

from 61 to 70 points

7 (seven) (D)

from 71 to 80 points

8 (eight) (C)

from 81 to 90 points

9 (nine) (B)

from 91 to 100 points

10 (ten) (A)

19.

Condition for signature and taking final exam

реализирани активности

20.

Language of instruction

македонски и англиски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature