Modelling and Simulation

Language of Instruction:Czech
Public info:http://www.fit.vutbr.cz/study/courses/IMS/public/
Private info:http://www.fit.vutbr.cz/study/courses/IMS/private/
Completion:credit+exam (written)
Type of
Guarantor:Peringer Petr, Dr. Ing. (DITS)
Deputy guarantor:Hrubý Martin, Ing., Ph.D. (DITS)
Lecturer:Hrubý Martin, Ing., Ph.D. (DITS)
Peringer Petr, Dr. Ing. (DITS)
Instructor:Hrubý Martin, Ing., Ph.D. (DITS)
Faculty:Faculty of Information Technology BUT
Department:Department of Intelligent Systems FIT BUT
Algorithms (IAL), DIFS
Discrete Mathematics (IDA), DMAT
Introduction to Programming Systems (IZP), DIFS
Mathematical Analysis (IMA), DMAT
Numerical Methods and Probability (INM), DMAT
Signals and Systems (ISS), DCGM
Substitute for:
Modelling and Simulation (MSI), DITS
Learning objectives:
  The goal is to introduce students to basic simulation methods and tools for modelling and simulation of continuous, discrete and hybrid systems.
  Introduction to modelling and simulation concepts. System analysis and classification. Abstract and simulation models. Continuous, discrete, and hybridd models. Heterogeneous models. Using Petri nets in simulation. Pseudorandom number generation and testing. Queuing systems. Monte Carlo method. Continuous simulation, numerical methods, Modelica language. Simulation experiment control. Visualization and analysis of simulation results.
Knowledge and skills required for the course:
  Basic knowledge of numerical mathematics, probability, statistics, and basics of programming.
Learning outcomes and competencies:
  Knowledge of simulation principles. The ability to create simulation models of various types. Basic knowledge of simulation system principles.
Why is the course taught:
  The algorithms and basic principles of modelling and simulation are frequently used for electrical circuit simulation, queuing sustems simulation, etc. The know-how can be used in other areas, too (for example computer games implementation).
Syllabus of lectures:
  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. Continuous systems modelling. Overview of numerical methods for continuous simulation. Introduction to Dymola simulation system.
  9. Combined/hybrid simulation, state events. Modelling of digital systems.
  10. Special model classes, models of heterogeneous systems. Model optimization.
  11. Analytical solution of queuing system models.
  12. Cellular automata and simulation.
  13. Checking of model validity, verification of models. Analysis of simulation results.
Syllabus of numerical exercises:
  1. discrete simulation: using Petri nets
  2. continuous simulation: differential equations, block diagrams, examples of models
Syllabus - others, projects and individual work of students:
 Individual selection of a suitable problem, its analysis, simulation model creation, experimenting with the model, and analysis of results.
Fundamental literature:
  • 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
Study literature:
  • 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
  • Texts available on course WWW page.
Controlled instruction:
  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.
Progress assessment:
  project, midterm exam, final exam (written)
Exam prerequisites:
  At least 10 points you can get during the semester

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