Discrete modeling

Entry requirements: Statistics, Set theory, combinatorics and graph theory.

Credits: 6

Semester: 1

Course: Core

Language of the course: English

Lecturer

Sergey Ivanov

Objectives

Students will get knowledge of modern methods of solving applied tasks, queuing systems, event models.

Contents

You'll learn the classification of methods and modern technologies for modeling, queueing systems, analytical models, Markov models, parallel and distributed simulation technology.
You will be able to develop a models of complex systems and distinguish between simulation methods.
You will learn the methods of development of discrete-event models using cellular automats, methods for statistical processing of simulation results, methods for verification and validation of models, methods of analysis of the results of the simulation.
You will learn how to use modern software environments for mathematical modeling and simulation. You will be able to plan experiments with the developed models; learn how to develop discrete and continuous models in Mathcad.

Format

Lectures and lab sessions.

Assessment

Examination.