COMPUTATIONAL MODELS OF ENVIRONMENTAL PHYSICS
| Title | ΥΠΟΛΟΓΙΣΤΙΚΑ ΠΡΟΤΥΠΑ ΦΥΣΙΚΗΣ ΤΟΥ ΠΕΡΙΒΑΛΛΟΝΤΟΣ / COMPUTATIONAL MODELS OF ENVIRONMENTAL PHYSICS |
| Code | ΥΦΕ302 |
| Faculty | Sciences |
| School | Physics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter |
| Coordinator | Konstantinos Karatzas |
| Common | No |
| Status | Active |
| Course ID | 600016931 |
Programme of Study: PMS YPOLOGISTIKĪ FYSIKĪ 2025
Registered students: 0
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses | 3 | 2 | 7.5 |
Programme of Study: Computational Physics
Registered students: 7
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses | 3 | 2 | 7.5 |
| Academic Year | 2025 – 2026 |
| Class Period | Winter |
| Faculty Instructors |
|
| Weekly Hours | 3 |
| Total Hours | 39 |
| Class ID | 600286323
|
Type Of Offer
- Disciplinary Course
Course Type 2021
Specialization / Direction
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600286323
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
Prerequisites
General Prerequisites
Calculus, Matrix Algebra, Computer programming
Learning Outcomes
Upon completion of the course, students will be able to perform data analysis and correlation analysis using basic and unsupervised Machine Learning procedures, recognize a problem as a classification or regression problem, understand how artificial neural networks and decision trees work, and apply these methods
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Make decisions
- Work autonomously
- Respect natural environment
- Advance free, creative and causative thinking
Course Content (Syllabus)
Part A
Introduction to Computational Models of Environmental Physics
Transport and Diffusion Models
Energy and Mass Balances in the Environment
Nonlinear Dynamical Models
Part B
Information Entropy, Classification, and Decision Trees
Data analysis, correlation analysis, Principal Component Analysis (PCA)
Data modeling. Regression models, Elements of Artificial Neural Networks (ANNs)
ANNs for unsupervised learning and knowledge extraction: Self-Organizing Maps (SOMs)
Information Entropy, Classification, Decision Trees
Educational Material Types
- Notes
- Slide presentations
- Interactive excersises
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 120 | ✓ | |||
| Reading Assigment | 40 | ✓ | |||
| Project | 40 | ✓ | ✓ | ||
| Written assigments | 20 | ✓ | |||
| Exams | 5 | ✓ | |||
| Total | 225 |
Student Assessment
Student Assessment methods
- Written Exam with Multiple Choice Questions (Summative)
- Written Assignment (Formative)
- Performance / Staging (Formative)
- Written Exam with Problem Solving (Summative)
Last Update
08-10-2025
