Computational Modelling Techniques

Learning Outcomes

  1. write algorithms in the computational area to efficient programming code
  2. apply programs for scientific computations
  3. use tools for performance optimisation and debugging
  4. analyze code and implement performance improvements
  5. report on performance in clear and correct writing
  6. illustrate the results of computation

Forms of Teaching

Lectures

Involment in lectures

Independent assignments

Project work

Laboratory

Laboratory work

Work with mentor

Lecturers consultations

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Laboratory Exercises 0 % 20 % 0 % 20 %
Seminar/Project 0 % 30 % 0 % 30 %
Mid Term Exam: Written 0 % 25 % 0 %
Final Exam: Written 0 % 25 %
Exam: Written 0 % 50 %

Week by Week Schedule

  1. Problem formulation
  2. Algorithm development, implementation, and verification
  3. Structure and documentation
  4. Math modules for numerical and scientific computing
  5. Math modules for numerical and scientific computing
  6. Math modules for numerical and scientific computing
  7. Program creation, compilation and linking, variables and parameters
  8. Midterm exam
  9. Flow control, subroutines and functions
  10. Flow control, subroutines and functions
  11. Use of libraries, internal and external communication, and interaction with other languages
  12. Use of libraries, internal and external communication, and interaction with other languages
  13. Numerical calculations, symbolic calculations, and graphics
  14. Importing and exporting information, Graphics with numerical and scientific computing software
  15. Final exam

Study Programmes

University graduate
Audio Technologies and Electroacoustics (profile)
Free Elective Courses (2. semester)
Communication and Space Technologies (profile)
Free Elective Courses (2. semester)
Computational Modelling in Engineering (profile)
(2. semester)
Computer Engineering (profile)
Free Elective Courses (2. semester)
Computer Science (profile)
Free Elective Courses (2. semester)
Control Systems and Robotics (profile)
Free Elective Courses (2. semester)
Data Science (profile)
Free Elective Courses (2. semester)
Electrical Power Engineering (profile)
Free Elective Courses (2. semester)
Electric Machines, Drives and Automation (profile)
Free Elective Courses (2. semester)
Electronic and Computer Engineering (profile)
Free Elective Courses (2. semester)
Electronics (profile)
Free Elective Courses (2. semester)
Information and Communication Engineering (profile)
Free Elective Courses (2. semester)
Network Science (profile)
Free Elective Courses (2. semester)
Software Engineering and Information Systems (profile)
Free Elective Courses (2. semester)

Literature

(.), Hans Petter Langtangen: A Primer on Scientific Programming with Python,
(.), Konstantinos Anagnostopoulos:Computational Physics - A Practical Introduction to Computational Physics and Scientific Computing,

For students

General

ID 223084
  Summer semester
5 ECTS
L3 English Level
L2 e-Learning
30 Lectures
39 Laboratory exercises

Grading System

86 Excellent
74 Very Good
62 Good
50 Acceptable