Simulation Tools and Techniques

• 39 hrs lectures
• 13 hrs projects

The basics of modeling and simulation theory. Understanding the principles of simulation system implementation. Knowledge of advanced simulation methods and techniques.

creation of simulation tools, models, and practical use of simulation techniques

Students will be introduced to design and implementation principles of simulation systems. Further, the methods and techniques for modeling and simulation of various types of models will be presented.

Basic knowledge of modelling, simulation, algorithms, and numerical mathematics.

• Rábová, Z. a kol.: Modelování a simulace, VUT Brno, 1992, ISBN 80-214-0480-9
• Cellier, F., Kofman, E.: Continuous System Simulation, Springer, 2006, ISBN: 978-0-387-26102-7
• Fishwick, P.: Simulation Model Design and Execution, Prentice Hall, 1995, ISBN 0-13-098609-7
• Soubor materiálů dostupný na WWW stránce předmětu.

• Fishwick, P.: Simulation Model Design and Execution, Prentice Hall, 1995, ISBN 0-13-098609-7
• Law, A., Kelton, D.: Simulation Modelling and Analysis, McGraw-Hill, 2000, ISBN 0-07-100803-9
• Zeigler, B., Praehofer, H., Kim, T.: Theory of Modelling and Simulation, second edition, Academic Press, 2000, ISBN 0-12-778455-1
• Ross, S.: Simulation, Academic Press, 2002, ISBN 0-12-598053-1
• Cellier, F., Kofman, E.: Continuous System Simulation, Springer, 2006, ISBN: 978-0-387-26102-7
• Fujimoto, R.: Parallel and Distribution Simulation Systems, John Wiley & Sons, 1999, ISBN:0471183830
• Chopard, B.: Cellular Automata Modelling od Physical Systems, Cambridge University Press, 1998, ISBN:0-521-67345-3

1. Introduction. Theory of modelling and simulation, DEVS formalism.
2. DEVS simulator.
3. Simulation systems: classification, principles of design and implementation. Simulation control algorithms. Parallel and distributed simulation.
4. Continuous simulation: numerical methods, stiff systems, algebraic loops. Dymola simulation system, Modelica language.
5. Discrete simulation: implementation of events and processes. Queueing systems.
6. Combined simulation: state events.
7. Modelling of systems described by partial differential equations. Basics of sensitivity analysis.
8. Digital systems simulation models and tools.
9. Cellular automata.
10. Models of uncertainty, using fuzzy logic in simulation.
11. Multimodels. Model optimization methods. Qualitative simulation.
12. Simulation experiment control, simulation results analysis. Introduction to model validation and verification. Visualization methods. User interfaces of simulation systems. Simulation and virtual reality.
13. Simulation system implementation case study. Examples of simulation models.

At least half of the points for each project.

There are no checked study.