Course detail
Modelling and Simulation
FIT-MSDAcad. year: 2019/2020
Simulation systems and their classification. Design and implementation of simulation systems. Special types of models. Multimodeling, multisimulation. Parallel and distributed simulation. Knowledge-based simulation, model optimization. Realtime and interactive simulation.
Language of instruction
Mode of study
Guarantor
Department
Learning outcomes of the course unit
Create, verify, and validate simulation models.
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Recommended reading
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
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
Zeigler B., Kim T., Praehofer H.: Theory of Modeling and Simulation. Academic Press Inc.,U.S.; 2nd Edition edition. 2000. ISBN: 0127784551
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
- Introduction. Types of problems, which can be solved using simulation methods. Dynamical systems theory.
- Architectures of simulation systems and their classification. Principles of simulation system design and implementation.
- Modeling and Simulation-Based Development of Systems. Hardware-in-the-loop, Human-in-the-loop, Model continuity.
- Multimodels, multiparadigm modeling and simulation, multiresolution modeling and simulation. Architectures of simulators.
- Examples of multiparadigm modeling: Processes, FSA, Petri nets, DEVS.
- Object-oriented and component approaches to modeling and simulation.
- Parallel and distributed simulation.
- Anticipatory systems. Nested simulation. Reflective simulation.
- Architectures for multisimulations. Cloning, independent time axes.
- Optimization, adaptation, learning.
- Modeling and simulation of intelligent systems. Sftcomputing and simulation.
- Architectures for multiagent simulations. Compex systems simulation.
- Visualization. Interactive simulation.
Guided consultation in combined form of studies
Teacher / Lecturer