Information Package / Course Catalogue
Machine Learning in Simulation-Based Engineering Design
Course Code: MME551
Course Type: Area Elective
Couse Group: Second Cycle (Master's Degree)
Education Language: English
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 8
Objectives of the Course

This course aims to integrate simulation-based engineering design with machine learning techniques. Students will learn how to combine CAD modeling, finite element analysis (FEA), and simulation data with machine learning methods to develop predictive, optimized, and data-driven engineering solutions.

Course Content

Simulation-based design principles, CAD and FEA integration, structural and multi-physics simulations, dataset generation from simulation environments, feature extraction, supervised machine learning methods, regression models, model validation, and integration of machine learning into engineering design optimization.

Name of Lecturer(s)
Learning Outcomes
1.1. Understand simulation-based engineering design principles
2.2. Apply finite element analysis in engineering problems
3.3. Generate datasets from simulation environments
4.4. Apply machine learning methods to engineering data
5.5. Develop predictive models based on simulation results
6.6. Integrate machine learning into engineering design workflows
Recommended or Required Reading
1.1. Martins, J. R., & Ning, A. (2021). Engineering design optimization. Cambridge University Press.
2.2. Ansys Parametric Design and Simulation Tutorials
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.".
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to simulation-based engineering design
Week 2 - Theoretical
CAD and simulation integration
Week 3 - Theoretical
Fundamentals of finite element analysis (FEA)
Week 4 - Theoretical
Structural simulation applications
Week 5 - Theoretical
Multi-physics simulation concepts
Week 6 - Theoretical
Simulation-based design optimization
Week 7 - Theoretical
Case studies in simulation-driven design
Week 8 - Theoretical
Dataset generation from simulation
Week 9 - Theoretical
Dataset generation from CAD and FEA
Week 10 - Theoretical
Feature extraction and engineering data
Week 11 - Theoretical
Introduction to machine learning
Week 12 - Theoretical
Regression models (XGBoost, Random Forest)
Week 13 - Theoretical
Regression models (XGBoost, Random Forest)
Week 14 - Theoretical
Model validation and performance evaluation
Week 15 - Theoretical
Model validation and performance evaluation
Week 16 - Final Exam
Final Project
Assessment Methods and Criteria
Type of AssessmentCountPercent
Assignment5%10
Project1%10
Midterm Examination1%20
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory1453112
Assignment57560
Project110515
Midterm Examination1426
Final Examination1426
TOTAL WORKLOAD (hours)199
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
PÇ-12
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
Adnan Menderes University - Information Package / Course Catalogue
2026