Modelling and Simulation

• 39 hrs lectures
• 4 hrs seminar
• 9 hrs projects

• 70 pts final exam (written part)
• 10 pts mid-term test (written part)
• 20 pts projects

The goal is to introduce students to basic simulation methods and tools for modelling and simulation of continuous, discrete and hybrid systems.
Result will be basic knowledge of systems modelling, simulation system implementation principles, and the ability to create simulation models of various types.

• Algorithms (IAL)
• Introduction to Programming Systems (IZP)
• Mathematical Analysis 2 (IMA2)
• Mathematical Analysis 1 (IMA1)
• Linear Algebra (ILG)
• Discrete Mathematics (IDM)
• Probability and Statistics (IPT)
• Signals and Systems (ISS)

Basic knowledge of numerical mathematics, probability, statistics, and basics of programming.

• Texts available on course WWW page.
• Fishwick P.: Simulation Model Design and Execution, PrenticeHall, 1995, ISBN 0-13-098609-7
• Law A., Kelton D.: Simulation Modelling and Analysis, McGraw-Hill, 1991, ISBN 0-07-100803-9
• Ross, S.: Simulation, Academic Press, 2002, ISBN 0-12-598053-1
• Modelica - A Unified Object-Oriented Language for Systems Modeling -
  Language Specification, Version 3.4, Modelica Association, 2017

• Fishwick P.: Simulation Model Design and Execution, PrenticeHall, 1995, ISBN 0-13-098609-7 Law A., Kelton D.: Simulation Modelling and Analysis, McGraw-Hill, 1991, ISBN 0-07-100803-9 Ross, S.: Simulation, Academic Press, 2002, ISBN 0-12-598053-1
• Cellier F., Kofman E.: Continuous System Simulation. Springer 2000.

1. Introduction to modelling and simulation. System analysis, classification of systems. Basic introduction to systems theory.
2. Model classification: conceptual, abstract, and simulation models. Multimodels. Basic methods of model building.
3. Simulation systems and languages, basic means of model and experiment description. Principles of simulation system implementation.
4. Generating, transformation, and testing of pseudorandom numbers. Stochastic models, Monte Carlo methods.
5. Parallel process modelling. Using Petri nets in simulation.
6. Models o queuing systems. Discrete simulation models.
7. Time and simulation experiment control, "next-event" algorithm.
8. Cellular automata and simulation.
9. Continuous systems modelling. Overview of numerical methods for continuous simulation. Introduction to Modelica.
10. Combined/hybrid simulation, state events. Modelling of digital systems.
11. Special model classes, models of heterogeneous systems, model parameters optimization overview.
12. Analytical solution of queuing system models.
13. Checking of model validity, verification of models. Analysis of simulation results.

1. discrete simulation: using Petri nets
2. continuous simulation: differential equations, block diagrams, examples of models

Individual selection of a suitable problem, its analysis, simulation model creation, experimenting with the model, and analysis of results.

Project, midterm exam, final exam (written). Exam prerequisites: At least 10 points you can get during the semester.
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.