Information Package / Course Catalogue
| Course Code | : MME639 |
| Course Type | : Area Elective |
| Couse Group | : Third Cycle (Doctorate Degree) |
| Education Language | : English |
| Work Placement | : N/A |
| Theory | : 3 |
| Prt. | : 0 |
| Credit | : 3 |
| Lab | : 0 |
| ECTS | : 8 |
This course aims to develop advanced knowledge in intelligent engineering design systems by integrating machine learning, simulation-based modeling, and optimization methodologies. Students will learn how to construct hybrid, data-driven and physics-based design frameworks for solving complex engineering problems and generating high-impact research outcomes.
Advanced engineering design systems, simulation-driven modeling, integration of machine learning with finite element analysis (FEA), hybrid modeling (physics-based + data-driven), surrogate modeling techniques, multi-objective optimization, uncertainty quantification, robust design strategies, intelligent system architectures, and research-oriented design methodologies.
| 1. | 1. Develop advanced intelligent engineering design frameworks |
| 2. | 2. Integrate machine learning with simulation-based engineering models |
| 3. | 3. Apply hybrid modeling techniques combining physics-based and data-driven approaches |
| 4. | 4. Construct surrogate models for complex engineering systems |
| 5. | 5. Solve multi-objective optimization problems |
| 6. | 6. Analyze uncertainty and robustness in engineering design |
| 7. | 7. Develop research-oriented design methodologies for publication |
| 1. | 1. Forrester, A., Sobester, A., & Keane, A. (2008). Engineering design via surrogate modelling: a practical guide. John Wiley & Sons. |
| 2. | 2. Rao, S. S. (2019). Engineering optimization: theory and practice. John Wiley & Sons. |
| 3. | 3. Géron, A. (2025). Hands-On Machine Learning with Scikit-Learn and PyTorch: Concepts, Tools, and Techniques to Build Intelligent Systems. " O'Reilly Media, Inc.". |
| Type of Assessment | Count | Percent |
|---|---|---|
| Assignment | 5 | %10 |
| Project | 1 | %10 |
| Midterm Examination | 1 | %20 |
| Final Examination | 1 | %60 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 14 | 5 | 3 | 112 |
| Assignment | 5 | 7 | 5 | 60 |
| Project | 1 | 10 | 5 | 15 |
| Midterm Examination | 1 | 4 | 2 | 6 |
| Final Examination | 1 | 4 | 2 | 6 |
| TOTAL WORKLOAD (hours) | 199 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | PÇ-6 | PÇ-7 | PÇ-8 | PÇ-9 | PÇ-10 | PÇ-11 | PÇ-12 | PÇ-13 | PÇ-14 | |
OÇ-1 | 3 | 3 | 3 | 3 | 4 | 3 | 4 | 3 | 3 | 4 | 3 | 3 | ||
OÇ-2 | 4 | 4 | 4 | 4 | 5 | 4 | 3 | 4 | 4 | 5 | 4 | 4 | ||
OÇ-3 | 3 | 3 | 5 | 4 | 5 | 4 | 4 | 4 | 5 | 5 | 4 | 3 | ||
OÇ-4 | 4 | 5 | 5 | 5 | 4 | 5 | 5 | 5 | 4 | 4 | 5 | 4 | ||
OÇ-5 | 3 | 5 | 5 | 3 | 4 | 4 | 4 | 3 | 3 | 4 | 5 | 4 | ||
OÇ-6 | ||||||||||||||
OÇ-7 | ||||||||||||||