COMPUTATIONAL METHODS IN APPLIED PHYSICS
| Title | ΥΠΟΛΟΓΙΣΤΙΚΕΣ ΜΕΘΟΔΟΙ ΕΦΑΡΜΟΣΜΕΝΗΣ ΦΥΣΙΚΗΣ / COMPUTATIONAL METHODS IN APPLIED PHYSICS |
| Code | ΥΦΕ210 |
| Faculty | Sciences |
| School | Physics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Spring |
| Coordinator | Michail Maragkakis |
| Common | No |
| Status | Active |
| Course ID | 600020685 |
Programme of Study: PMS YPOLOGISTIKĪ FYSIKĪ 2025
Registered students: 0
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses | 2 | 1 | 7.5 |
Programme of Study: Computational Physics
Registered students: 12
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses | 2 | 1 | 7.5 |
| Academic Year | 2025 – 2026 |
| Class Period | Spring |
| Instructors from Other Categories |
|
| Weekly Hours | 3 |
| Total Hours | 39 |
| Class ID | 600286343
|
Course Type 2021
Specialization / Direction
Course Type 2016-2020
- Scientific Area
Course Type 2011-2015
Knowledge Deepening / Consolidation
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600286343
Language of Instruction
- Greek (Instruction, Examination)
Learning Outcomes
- Break down the problems they're dealing with into smaller parts that are solvable and manageable
- Use high-efficiency methodologies and algorithms
- Select the optimal method/algorithm for solving classes of problems focused on the specific scientific field of their interest
- Practice on specific problems that interest them personally and come from any field of physics they wish (with their own choice of problem)
- Collect and/or manage large volumes of data on these problems based on the methodologies/algorithms they have learned
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Work autonomously
- Work in an interdisciplinary team
- Generate new research ideas
- Advance free, creative and causative thinking
Course Content (Syllabus)
• Introduction to concepts of algorithms, flowcharts, and pseudocode.
• Basic examples of pseudocode and flowchart usage.
• General algorithms for data processing (search). Hands-on exercises.
• General algorithms for data processing (sorting). Hands-on exercises.
• Algorithm efficiency (Big O notation) and comparison of the efficiency of previous algorithms.
• Brief introduction to graphs. Graph types and topologies. General algorithms for graph creation.
• Selection of semester project topics for each student with interactive discussion.
• Metrics in graphs. Topology analysis techniques. Hands-on exercises.
• Introduction to the concept of dynamic graphs/systems and their analysis methodology. Hands-on exercises.
• Time series and their use in physics studies/problems.
• Multi-level and multiplex graphs with examples.
• Presentation of application examples for all of the above cases.
• Recapitalization - Summary.
Educational Material Types
- Notes
- Slide presentations
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 85 | ✓ | |||
| Reading Assigment | 90 | ✓ | |||
| Written assigments | 45 | ✓ | ✓ | ||
| Exams | 5 | ✓ | |||
| Total | 225 |
Student Assessment
Student Assessment methods
- Written Assignment (Formative, Summative)
- Performance / Staging (Summative)
Bibliography
Course Bibliography (Eudoxus)
Δίνονται σημειώσεις
Additional bibliography for study
K. Αναγνωστόπουλος, Υπολογιστική Φυσική, Κάλλιπος
Last Update
08-10-2025
