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Modeling and simulation of engineering systems

Course Description: Importance of modeling and simulation, different types of modeling, continuous and discrete models, model components, descriptive variables and exploratory data analysis, classification vs. regression models, numerical simulation, random number generators, principal component analysis and feature importance analysis, types of regression and classification algorithms, hybrid techniques, model selection, Monte Carlo simulation, object-oriented and agent-based simulation, system dynamics and systems thinking, introduction to software packages, solving real-world problems using machine learning models.

Credit hours: 3

Objectives of the course :

1. Enable the student to use modern software packages in the analysis of real-world data.

Enhance the student's understanding of the technical foundations of modern modeling and computer simulation software.

3. Developing students' skills in selecting and applying appropriate analytical techniques to a wide range of real-world problems and datasets.

4- Develop and enhance the student's understanding of different types of modeling strategies and model selection techniques.

5- Enhancing the student's ability to summarize analysis results and present them in a clear and coherent manner.

6. Enabling the student to model and analyze real-world problems using the latest specialized software packages.

Course outputs :

Knowledge and understanding:
1.1 Explanation of the importance of modeling and simulation, and continuous and discrete models.
1.2 Definition of model components, descriptive variables, and exploratory data analysis.
1.3 Understanding the concept of state-space representation, classifying dynamic model systems, and random number generators.
1.4 Retrieval of concepts for Monte Carlo simulation, object-oriented simulation, and agent-based simulation.
1.5 Statement of Regression and Classification Algorithm Types, Hybrid Techniques, and Model Selection Methods.

Skills:
2.1 Applying different model types and their validation.
Choosing the appropriate modeling method.
2.3 Use of appropriate modeling technology according to the nature of the problem.
2.4 Interpretation of Modeling Problems Outputs and Results.
2.5 Using advanced digital technologies and ICT tools to solve modeling problems.

Values, autonomy and responsibility:

3.1 Demonstrate integrity and professionalism when dealing with various modeling challenges

Additional information:

Last updated: 2026-05-14