Study-Unit Description

Study-Unit Description


CODE CCE3502

 
TITLE Computer Modelling and Simulation

 
LEVEL 03 - Years 2, 3, 4 in Modular Undergraduate Course

 
ECTS CREDITS 5

 
DEPARTMENT Communications and Computer Engineering

 
DESCRIPTION Three fundamental topics in modelling and simulation are studied in this study-unit -Stochastic and discrete event Simulation Paradigms, Models estimated from data and Statistical Analysis of the Output.

An applied approach is adopted and practical examples that reinforce the techniques studied are cited and discussed. This study-unit is delivered from a computing point of view and it is therefore expected that the student is conversant in a high level programming language.

This study-unit is a useful companion/complementary unit to other units such as discrete event systems, networks, pattern recognition, machine learning and other courses in computer science and computer engineering.

Study-unit Aims:

- To learn the modelling methodology;
- To study the most useful and widely applicable modelling techniques;
- To study the most useful and widely applicable stochastic and discrete event computer simulation techniques;
- To learn how to develop good and robust computational models;
- To explore qualitatively and in a wider context the area of modelling and computer simulation.

Learning Outcomes:

1. Knowledge & Understanding:
By the end of the study-unit the student will be able to:

- Differentiate between dynamic and static models;
- Differentiate between discrete event systems and continuous-time systems;
- Specify a random number generator and derive the mathematical models required to generate random variables and model inputs;
- Test a random number generator;
- Code static modelling techniques in a high level computer language;
- Code a discrete event simulation in a high level language;
- Formulate a Monte-Carlo simulation and analyse the output;
- Solve stochastic problems analytically;
- Compare simulation results to analytical results;
- Manually recognize patterns in data and select a model that fits the pattern;
- Understand the difference between linear regression and logistic regression models;
- Numerically fit a regression model to data;
- Understand the local and global optimisation problem;
- Derive and Implement a gradient based minimisation method;
- Formulate a probabilistic model in a simple classification problem;
- Formulate a probabilistic Markov model for times series modelling;
- Implement a simple times series model that fits data;
- Differentiate between time-domain Data-Driven Models and a Physics Models;
- Formulate a simple difference equation model motivated by qualitative analysis and verify the model;
- Numerically compute the difference equation coefficients;
- Compare the complexity of the difference equation model to that of a physics-based model;
- Implement a continuous-time system model on a discrete event simulator as a mixed mode simulation environment;
- Analyse and validate the simulation output;
- Provide a measure of accuracy for the output data.

2. Skills:
By the end of the study-unit the student will be able to:

- Write down and elaborate on the problem definition and abstraction of a model;
- Adapt an algorithm or algorithms or methods that can be computed on a digital computer;
- Validate, optimize and tune a model;
- Provide a critical review of the model output;
- Coding the algorithm/s in Python.

Main Text/s and any supplementary readings:

- Discrete-event Simulation - A first Course, L. M. Leemis and S. K. Park, Pearson.
- A first Course in Mathematical Modeling, Frank R Giordano, William P. Fox, Steven B. Horton and Maurice D. Weir, Brooks/Cole.
- Simulation Modelling and Analysis, Averill M. Law, McGraw-Hill.
- A Guide to Simulation, Paul Brately, Bennett L. Fox and Linus E Schrage, 2nd ed., Springer.
- Fundamentals of Machine Learning for Predictive Data Analytics, J. D. Kelleher, B. Mac-Namee and A. D’Arcy.

 
ADDITIONAL NOTES Pre-requisite Qualifications: Computer Programming in a High-Level Language and Mathematics courses in Linear Algebra, Statistics and Probability

 
STUDY-UNIT TYPE Lecture and Independent Study

 
METHOD OF ASSESSMENT
Assessment Component/s Assessment Due Resit Availability Weighting
Examination (1 Hour) SEM2 Yes 40%
Assignment SEM2 Yes 20%
Assignment SEM2 Yes 20%
Assignment SEM2 Yes 20%

 
LECTURER/S Adrian F. Muscat

 
The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints.
Units not attracting a sufficient number of registrations may be withdrawn without notice.
It should be noted that all the information in the study-unit description above applies to the academic year 2019/0, if study-unit is available during this academic year, and may be subject to change in subsequent years.

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