Simulation Tools and Techniques

• 39 hrs lectures
• 13 hrs projects

• 70 pts final exam
• 30 pts projects

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.
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 methods.

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

• Zeigler B., Praehofer H., Kim T.: Theory of Modelling and Simulation, 2nd edition, Academic Press, 2000
• Law, A., Kelton, D.: Simulation Modelling and Analysis, McGraw-Hill, 2000, ISBN 0-07-100803-9
• Soubor materiálů dostupný na WWW stránce předmětu.
• Cellier, F., Kofman, E.: Continuous System Simulation, Springer, 2006, ISBN: 978-0-387-26102-7
• Chopard, B.: Cellular Automata Modelling od Physical Systems, Cambridge University Press, 1998, ISBN:0-521-67345-3
• Fujimoto, R.: Parallel and Distribution Simulation Systems, John Wiley & Sons, 1999, ISBN:0471183830
• Nutaro, J.: Building Software for Simulation: Theory and Algorithms, with Applications in C++. John Wiley & Sons, 2011, ISBN-13: 978-0470414699

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

• Individual solution of specified simulation problem, or extending of given simulation system to allow the use of new modelling methods.

Within this course, attendance on the lectures is not monitored.
The knowledge of students is examined by the projects and
by the final exam. The minimal number of points which
can be obtained from the final exam is 30. Otherwise,
no points will be assigned to a student.