Modelling and Simulation

Code:

M.IA029

Acronym:

MS

Keywords
Classification	Keyword
OFICIAL	Computer Science
OFICIAL	Informatics Engineering

Instance: 2025/2026 - 1S

Active?	Yes
Responsible unit:	Department of Informatics Engineering
Course/CS Responsible:	Master in Artificial Intelligence

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M.IA	35	Syllabus	1	-	6	39	162
			2

Teaching language

English

Objectives

The information on this page is temporary and will be updated at the beginning of the academic year.

Learning outcomes and competences

The goals of this course unit are:
1. to present the basic concepts of modelling and simulation and their areas of application;
2. to present the different phases of the life cycle of a simulation project;
3. to present the main types of models and their adequacy to the treatment of different problems;
4. to present the main architectural aspects of modelling and simulation tools;
5. to introduce modelling and simulation tools.
After the successful completion of this course, students should be capable of:
1. Identifying problems, developing models and simulation projects;
2. Analysing and applying simulation results;
3. Including simulation techniques in the scientific method of different areas of research;
4. Showing their knowledge of the main areas of interest and current challenges in the area of modelling and simulation;
5. Using modelling and simulation tools;
6. Designing and implementing tools and simulation environments for special or general purposes.

Working method

Presencial

Program

1. Review and presentation of concepts:
a. Simulation as an engineering methodology;
b. Modelling (realism, abstraction) and types of models (normative, behavioural….);
c. Modelling metaphors and simulation techniques;
d. Data preparation and treatment;
e. Modelling of complex systems and stochastic processes;
2. Simulation projects:
a. Basic techniques of simulation: continuous, discrete and probabilistic simulation;
b. Simulation life cycle: data modelling, collection and preparation; test, calibration and validation of models, results analysis and implementation;
c. Languages and environments of simulation.
3. Advanced topics in modelling and simulation:
a. Object-oriented simulation;
b. Distributed simulation;
c. Visual interactive modelling and simulation;
d. Intelligent simulation;
e. Agent-based modelling and simulation;
f. Simulation environments and simulation of environments;
4. Advanced applications of simulation;
5. Project.

Mandatory literature

Osais, Y.E; Computer Simulation: A Foundational Approach Using Python
Downey, A.B. ; Modeling and Simulation in Python
Law, A.M.; Simulation Modeling and Analysis (5th ed.)
Banks, J., Carson II, J.S., Nelson, B.L., & Nicol, D.M. (2014); Discrete-Event System Simulation (5th ed.)
Teixeira, J.M.F., Brito, A.E.S.C. (2006); Simulação por computador
Chung, C.A. (2004); Simulation Modeling Handbook: A Practical Approach

Teaching methods and learning activities

Teaching methodology will include:
- Theory classes, to present and to discuss conceptual aspects;
- Practical tutorial-based classes, to develop a simulation project;
- Development of a simulation project;
- Paper writing-up;
- Oral presentation of project results.

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Participação presencial	10,00
Apresentação/discussão de um trabalho científico	10,00
Trabalho escrito	20,00
Trabalho prático ou de projeto	60,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Elaboração de projeto	60,00
Frequência das aulas	42,00
Trabalho escrito	60,00
Total:	162,00

Eligibility for exams

To be admitted in the assessment process, students have to reach markings >=8.0 in the continuous assessment (CA) components.

Calculation formula of final grade

Final mark: CA = 0.1*SP + 0.6*P + 0.2*R + 0.1*D