ELEC 498/898 (Spring 2017)
Transatlantic Course on Computational Modeling and Simulation I: Discrete Systems
MF: 8:00-9:30 AM (UNL); 3:00-4:30 PM (TUC)
This course will be offered jointly by the Dept. of ECE, UNL and Modeling and Simulation Science Center, Technical University of Clausthal (TUC), Germany.
Goal: To introduce the fundamental concepts of computational modeling and simulation of discrete systems and their applications, ranging from description and development of a model to simulation and analysis of their results using simulation software. Modeling technciques, including Monte Carlo Simulation, and varification and validation in the context of a design will be covered.
Prereq.: Probability and statistics (ELEC 305), a course in high level programming language
Course outline:
Intro to discrete-event simulation
-Areas of application; system environment; components of a system; model of a system; types of model
Simulation Examples
-How to simulate randomness; queuing simulation with single and multiple servers; estimating the distribution of lead-time demand
Concepts in discrete-event simulation
-Event scheduling; time advance algorithm; list processing; dynamic allocation and linked lists in list processing
Simulation Software
-History of Simulation Software; classification of simulation packages; desirable software features; hardware and software requirements; examples
Review of basic probability and statistics concepts
-PMF, CDF, central limit theorem, laws of large numbers, expectation, variance, probability distribution functions
Random number generation
-Linear congruential method; tests for randomness; random variable generation
Mathematical and Statistical Models
-Simulation output data and stochastic processes; estimation of mean, variances and correlation; confidence interval and hypothesis testing
Queuing models
-Characteristics of queuing systems; queuing notation; single and multiple server queues; steady state behavior of infinite and finite population models; networks of queues
Analysis of simulation data
-Input modeling; data collection; sample mean; sample variance
Monte Carlo Simulation and its application
Verification and Validation of Simulation Models
-Validation of model assumptions; validation of input-output transformation
Case studies
For more info, contact Dr. Vakilzadian @hvakilzadian@unl.edu