Course detail

Simulation Tools and Techniques

FIT-SNTAcad. year: 2019/2020

Theory of modelling and simulation, DEVS (Discrete Event System
Specification) formalism. Simulation systems, their design and
implementation. Algorithms used for simulation control, introduction to
parallel and distributed simulation. Continuous, discrete, and combined
simulation: model description methods, simulation tools, numerical
methods. Special types of models; corresponding methods, techniques, and
tools. Modeling of systems described by partial differential equations.
Introduction to model validation and verification. Simulation
experiment control. Simulation results analysis and visualization
overview. Simulation system case study.

Supervisor

prof. RNDr. Milan Češka, CSc.

Department

Department of Intelligent Systems (UITS)

Learning outcomes of the course unit

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.

Prerequisites

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

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Rábová, Z. a kol.: Modelování a simulace, VUT Brno, 1992, ISBN 80-214-0480-9
Zeigler B., Praehofer H., Kim T.: Theory of Modelling and Simulation, 2nd edition, Academic Press, 2000
Soubor materiálů dostupný na WWW stránce předmětu.
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
Nutaro, J.: Building Software for Simulation: Theory and Algorithms, with Applications in C++. John Wiley & Sons, 2011, ISBN-13: 978-0470414699

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

At least half of the points for each project.

Language of instruction

Czech

Work placements

Not applicable.

Aims

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.

Specification of controlled education, way of implementation and compensation for absences

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.

Classification of course in study plans

  • Programme IT-MGR-2 Master's

    branch MBI , any year of study, summer semester, 5 credits, compulsory-optional
    branch MPV , any year of study, summer semester, 5 credits, compulsory-optional
    branch MGM , any year of study, summer semester, 5 credits, compulsory-optional
    branch MSK , any year of study, summer semester, 5 credits, elective
    branch MIS , any year of study, summer semester, 5 credits, elective
    branch MBS , any year of study, summer semester, 5 credits, compulsory-optional
    branch MMI , any year of study, summer semester, 5 credits, compulsory-optional
    branch MMM , any year of study, summer semester, 5 credits, compulsory-optional

  • Programme MITAI Master's

    specialization NADE , any year of study, summer semester, 5 credits, elective
    specialization NBIO , any year of study, summer semester, 5 credits, elective
    specialization NGRI , any year of study, summer semester, 5 credits, elective
    specialization NNET , any year of study, summer semester, 5 credits, elective
    specialization NVIZ , any year of study, summer semester, 5 credits, elective
    specialization NCPS , any year of study, summer semester, 5 credits, elective
    specialization NSEC , any year of study, summer semester, 5 credits, elective
    specialization NEMB , any year of study, summer semester, 5 credits, elective
    specialization NHPC , any year of study, summer semester, 5 credits, elective
    specialization NISD , any year of study, summer semester, 5 credits, elective
    specialization NIDE , any year of study, summer semester, 5 credits, elective
    specialization NISY , any year of study, summer semester, 5 credits, compulsory
    specialization NMAL , any year of study, summer semester, 5 credits, elective
    specialization NMAT , any year of study, summer semester, 5 credits, elective
    specialization NSEN , any year of study, summer semester, 5 credits, elective
    specialization NVER , any year of study, summer semester, 5 credits, compulsory
    specialization NSPE , any year of study, summer semester, 5 credits, elective

  • Programme IT-MGR-2 Master's

    branch MIN , 1. year of study, summer semester, 5 credits, compulsory

Type of course unit

 

Lecture

39 hours, optionally

Teacher / Lecturer

prof. RNDr. Milan Češka, CSc.

Syllabus


  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.

Project

13 hours, compulsory

Teacher / Lecturer

prof. RNDr. Milan Češka, CSc.

Syllabus


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