IV109 Modeling and Simulation

Faculty of Informatics
Spring 2021
Extent and Intensity
2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
doc. Mgr. Radek Pelánek, Ph.D. (lecturer)
RNDr. Jiří Glozar (assistant)
Guaranteed by
doc. Mgr. Radek Pelánek, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics
Timetable
Fri 10:00–11:50 Virtuální místnost
  • Timetable of Seminar Groups:
IV109/01: each odd Thursday 8:00–9:50 Virtuální místnost, R. Pelánek
IV109/02: each even Thursday 8:00–9:50 Virtuální místnost, R. Pelánek
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 70 student(s).
Current registration and enrolment status: enrolled: 3/70, only registered: 0/70, only registered with preference (fields directly associated with the programme): 0/70
fields of study / plans the course is directly associated with
Course objectives
The course offers a wide overview of computational modeling and gives students a practical experience with computational modeling.
Learning outcomes
At the end of the course students will be able to: describe main concepts of complex systems (particularly "feedback loops"); explain main principles and applications of computational modeling; compare modeling approaches; describe well-know case studies in computational modeling; create a computational model.
Syllabus
  • Introduction, history, role of modeling and simulation in research, applications. Computational models.
  • Complex systems, system thinking, feedback loops.
  • System dynamics approach, examples (demographics, Limits to growth).
  • Agent based modeling: basic principles, cellular automata, decentralized systems.
  • Game theory, models of cooperation. Models of adaptation (genetic algorithms, neural networks).
  • Modeling of networks: examples of networks and their properties, models of networks.
  • Analysis and evaluation of models.
  • Application of modeling from different areas (e.g. economics, traffic, epidemiology, biology).
Literature
    recommended literature
  • PELÁNEK, Radek. Modelování a simulace komplexních systémů. Jak lépe porozumět světu (Modeling and simulation of complex systems). 1st ed. Brno: Masarykova univerzita, 2011, 236 pp. mimo edice. ISBN 978-80-210-5318-2. info
  • RESNICK, Mitchel. Turtles, termites, and traffic jams : explorations in massively parallel microworlds. Cambridge: Bradford Book, 2000, xviii, 163. ISBN 0-262-68093-9. info
  • BARABÁSI, Albert-László. Linked :how everything is connected to everything else and what it means for business, science, and everyday life. New York: Plume Book, 2003, 294 s. ISBN 0-452-28439-2. info
Teaching methods
lectures, software labs
Assessment methods
40% project (modeling and simulation of a choosen problem), 40% written exam, 20% reading assignments during semester
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://www.fi.muni.cz/~xpelanek/IV109/
The course is also listed under the following terms Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2022, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (Spring 2021, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2021/IV109