Course CSE309 · Year III · Summer 2025-2026

MODELING AND SIMULATION

Compulsory course in Computer Science (in English), taught by Marcela Nicoleta Breaz.

This course page is from 2025-2026 and is archived.
See this course for the current academic year

Overview

Lecturer
Marcela Nicoleta Breaz
Seminar tutor
Adriana Bîrluțiu
Type of course
Compulsory
Language of instruction
English
Erasmus language
English
Domain
Computer Science
Field of study
Computer Science (in English)
Form of education
Full-time
Form of instruction
Class / Seminary
Credit awarded by
Grade
Teaching methods
Lecture, discussion, exemplification.
Entry requirements
Recommended but not mandatory: 1. Probability and mathematical statistics 2. Mathematical software CSE206 3. Numerical calculus 4. Differential and partial derivatives equations

Aims

The general aim related to this course consists in getting knowledge which helps the students to use the mathematical concepts together with a specific software to model phenomena from various fields as medicine, physics, chemistry, economy, sociology etc..

A specific aim is related to the use of Matlab and Excel software to model various reality-based problems with mathematical tools.

Besides the knowledge of basics mathematical modeling aided by software products, the course is focused also on the development of an open minded approach of the interdisciplinary matter.

Course contents

I. Elements of mathematical modeling and simulation 1. Introduction 2. Process of mathematical modeling 3. Models obtained through the translation of the problem in mathematical language 4. Simulation techniques and random numbers II. Models based on statistical techniques 1. Simple linear regression model 2. Polynomial regression model 3. Other simple regression models 4. Multiple linear regression models 5. Other multiple regression models 6. Dynamic models III. Models based on optimization techniques 1. Elements of mathematical programming 2. Transportation problems 3. Problems related to production and stocking 4. Problems of mixtures (dietary optimization, alloy mixture optimization) 5. Problems of cutting-stock 6. Problems from games theory 7. Other optimization problems IV. Deterministic models based on equations 1. Problems of populations’ dynamic 2. Deterministic models in epidemiology 3. Deterministic models in physics

Learning outcomes

• Identifying the appropriate models and methods for solving real-life problems; • Giving the interpretation of mathematical and computer science (formal) models; • Using the simulation in the study of the behavior of developed models and evaluation of results; • Embedding the formal models in specific applications in various domains.

Assessment

Practical project – 50%; continuous assessment – 50%.

Recommended reading

An introduction to mathematical modeling techniques
E.A. Bender
Dover Books, New York, 2000 · -
Mathematical modeling and simulation, theory and applications
N. Breaz
Electronic version in the university library, Alba Iulia, 2009 · -
MATLAB Guide, 2nd edition
D. J. Higham, N. J. Higham,
SIAM, -, 2005 · -
A tutorial on Mathematical Modeling
M. P. McLaughlin
www.causascientia.org/math_stat/Tutorial.pdf, -, 1999 · -
Numerical Computing in MATLAB
Cleve Moler
SIAM, -, 2005 · -