Advanced Methods of Modeling and Simulation
||Science and Technology Programming
Instance: 2018/2019 - 1S
Cycles of Study/Courses
||No. of Students
Teaching Staff - Responsibilities
The aims of this course unit may be considered in two perspectives. First of all, this course unit aims to present the area of modelling and simulation as an important resource and indispensible tool in the scientific method in order to test and validate concepts and theories, which are useful in almost all PhD projects. Subsequently, the presentation and discussion of recent techniques and methods of modelling and simulation should motivate the identification of problems and challenges, which make this area an excellent study subject in the domain of computer engineering.
In more detail, the aims of this course unit are:
- to present the basic concepts of (computational) modelling and simulation and their diverse areas of application;
- to present the different phases of the life cycle of a simulation project, from the modelling phase to the analysis and application of results;
- to present the main types of models and their adequacy to the treatment of different problems, as well as the main simulation techniques.
- to extensively present and discuss the state of the art of recent technology in the area of modelling and simulation, by identifying the challenges, main areas of investigation and trends.
Learning outcomes and competences
After the successful completion of this course unit, students should be capable of:
- Identifying problems, developing models and simulation projects;
- Analysing and applying simulation results;
- Including simulation techniques in the scientific method of different areas of investigation;
- Showing their knowledge of the main areas of interest and current challenges in the area of modelling and simulation;
- Using modelling and simulation tools;
- Designing and implementing tools and simulation environment for special or general purposes.
1. Review and presentation of concepts:
a. Simulation as an engineering methodology;
b. Modelling (realism, abstraction) and types of models (normative, behavioural….);
c. Data preparation and treatment;
d. 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. Realistic simulation;
e. Intelligent simulation;
f. Agent-based modelling and simulation;
g. Simulation environments and simulation of environments;
h. Learning and evolutionary models;
i. Optimisation methods in simulation;
j. Heuristic and metaheuristic;
4. Advanced applications of simulation.
Brito, A.; Teixeira, J.; Simulação por computador: fundamentos e implementação em C e C++
, Publindústria, 2001
Law, A.; Simulation Modeling and Analysis
, McGraw-Hill, 2007
Chung, C. ; Simulation Modeling Handbook: a practical approach
, CRC Press, 2003
Banks, J.; Carson, J.; Nelson, B. ; Discrete-event System Simulation
, Prentice Hall, 2005
Vários Autores; Artigos de Conferências e Revistas Científicas da área, ACM SIGSIM, SCS, IEEE, Eurosis
Teaching methods and learning activities
Passive learning is limited to the initial introduction of concepts. Students will be encouraged to deepen their knowledge by contextualising the state of the art and identifying recent trends. This component will comprise presentations of recent and relevant case studies of the different areas of study taught in this course unit.
However, the principal teaching method is based on an active learning, being directed to research and project. It aims to integrate students in practical activities, such as the use of simulation tools, implementation of ad-hoc simulators, reviews and readings of scientific papers, as well as structuring students’ knowledge by attending seminars and writing articles.
Technological sciences > Engineering > Simulation engineering
Distributed evaluation without final exam
Amount of time allocated to each course unit
|Elaboração de projeto
|Frequência das aulas
|Trabalho de investigação
Eligibility for exams
To be admitted to exams, students have to reach a grade >=7 in the continuous assessment (CA) component.
Calculation formula of final grade
Continuous Assessment (CA) comprises:
- Preparation/presentation of seminars (S) (20%)
- Preparation/presentation of tutorial (S) (20%)
- Execution of a simulation project (P) (60%)
Final Project (P) comprises:
- Oral presentation + demo (60%)
- Written paper (40%)
Final Mark (FM) will be based on the following formula:
FG= 0,2 *S + 0,2*T + 0,6*P
The student cannot get a mark below 8 in each of the components, in which case he/she would fail the course.
Examinations or Special Assignments
- Writing an article and its presentation; - Preparation and presentation of seminars; - Medium-size simulation project.
Special assessment (TE, DA, ...)
The assessment components are compulsory to all students, even to the ones who do not need to attend classes. The assessment activities will take place according to students’ availability.
Students may improve their grades by improving one or more of the following assessment components: - Simulation project; - Writing up a paper and its presentation; - Preparation/presentation of seminars. - Mini-test assessment