Course details

Modelling and Simulation

MSD Acad. year 2020/2021 Winter semester

Classification of systems and models. Different ways of describing models. Methodology of model construction, special types of models. Principles of implementation of simulation systems. Multiparadigmatic modeling and simulation. Parallel and distributed simulation. Real time simulation, interactive simulation.

The state examination topics:

  1. Theory of modeling and simulation, DEVS and its variants.
  2. Combined systems (continuous simulation and discrete events).
  3. Stochatic Petri nets.
  4. High-level petri nets.
  5. Interpreted Petri nets and control system modeling.
  6. Real-time simulation and interactive simulation.
  7. Anticipatory systems, nested and reflective simulations.
  8. Parallel and distributed simulation.
  9. Multisimulation, cloning of simulations, applications.
  10. Softcomputing and machine learning in systems modeling and simulation.

Guarantor

Language of instruction

Czech

Completion

Examination (written+oral)

Time span

39 hrs lectures

Assessment points

70 exam, 30 projects

Department

Lecturer

Instructor

Subject specific learning outcomes and competences

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

Generic learning outcomes and competences

Ability to create system models and use simulation to solve problems.

Learning objectives

Students will be introduced to design and implementation principles of simulation systems. Further, the techniques for modelling and simulation of various types of models will be presented. Special attention is paid to advanced simulation techniques including artificial intelligence.

Study literature

  • Fishwick P.: Simulation Model Design and Execution, PrenticeHall, 1995
  • Law A., Kelton D.: Simulation Modelling and Analysis, McGraw-Hill, 1991
  • Didier H. Besset: Object-Oriented Implementation of Numerical Methods: An Introduction with Java & Smalltalk, Morgan Kaufmann, 2000
  • Zeigler B., Kim T., Praehofer H.: Theory of Modeling and Simulation. Academic Press Inc.,U.S.; 2nd Edition edition. 2000. ISBN: 0127784551
  • Sarjoughian H., Cellier F.: Discrete Event Modeling and Simulation Technologies: A Tapestry of Systems and AI-Based Theories and Methodologies. Springer-Verlag New York Inc. 2001. ISBN: 0387950656
  • David R., Alla H,: Discrete, Continuos and Hybrid Petri Nets, Springer Verlag, 2010
  • Janoušek, V.: Simulace a návrh vyvíjejících se systémů. Brno, CZ: Fakulta informačních technologií VUT v Brně, 2009.

Fundamental literature

  • Fishwick P.: Simulation Model Design and Execution, PrenticeHall, 1995
  • Law A., Kelton D.: Simulation Modelling and Analysis, McGraw-Hill, 1991
  • Ross S.: Simulation, Academic Press, 2002
  • Zeigler B., Kim T., Praehofer H.: Theory of Modeling and Simulation. Academic Press Inc.,U.S.; 2nd Edition edition. 2000. ISBN: 0127784551
  • Sarjoughian H., Cellier F.: Discrete Event Modeling and Simulation Technologies: A Tapestry of Systems and AI-Based Theories and Methodologies. Springer-Verlag New York Inc. 2001. ISBN: 0387950656
  • David R., Alla H,: Discrete, Continuos and Hybrid Petri Nets, Springer Verlag, 2010

Syllabus of lectures

  • Classes of problems solvable by simulation methods. Theory of dynamical systems.
  • DEVS formalism and its variants.
  • Architectures of simulation systems, principles of implementation.
  • Examples of paradigm combinations - processes, Petri nets, DEVS and continuous systems.
  • Interpreted Petri nets. Control systems modeling.
  • Systems development based on modeling and simulation, real-time simulation, hardware-in-the-loop, human-in-the-loop, continuity model.
  • Object-oriented and component approaches to modeling and simulation.
  • Parallel and distributed simulation.
  • Anticipatory systems. Nested simulation. Reflective simulation. 
  • Multisimulations, cloning, independent time axes.
  • Optimization, adaptation, learning.
  • Modeling and simulation of intelligent systems. Softcomputing and simulation.
  • Multiagent simulations. Complex systems simulation.

Syllabus - others, projects and individual work of students

  • Essay based on selected scientific paper dealing with modeling and simulation of systems.

Progress assessment

Short tests in lectures, state of essay elaboration.

Controlled instruction

Lectures and essay elaboration.

Course inclusion in study plans

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