(ECEN 898) Computational Modeling and Simulation I: Discrete Systems
When: Wednesday and Friday, 3:30pm-4:45pm
Instructor: Dr. Hamid Vakilzadian
The goal of this course is to introduce the fundamental concepts of
computational modeling and simulation of discrete systems and their
applications that range from the development of a model to the
simulation and analysis of their results. Modeling techniques, including
Monte Carlo Simulation, Markov chain, verification and validation, and
sensitivity analysis in the design context, are covered.
Prereq.: A course in probability and/or statistics, a course in a high-level programming language.
Course outline:
Introduction to Discrete Systems Modeling:
Components of a system; model of a system; types of model, areas of application
Concepts in discrete-event simulation:
Event scheduling; time advance algorithm; dynamic allocation
Review of basic probability and statistics concepts:
PMF, CDF, central limit theorem, laws of large numbers, expectation, variance, probability distribution functions, correlation
Random number generation:
Linear congruential method; tests for randomness; random variable generation
Mathematical and Statistical Models:
Simulation of output data and stochastic processes; estimation of
mean, variance, 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 applications
Verification and Validation of Simulation Models:
Validation of model assumptions; validation of the input-output transformation, Techniques for increasing model validity, Sensitivity analysis of model parameters
Markov Processes:
Probabilistic systems, discrete time Markov processes, random walks
Please contact Dr. Vakilzadian for any questions: hvakilzadian1@unl.edu