Modeling and Simulation

Probability and Statistics

This course covers an introduction to modeling and simulation of real systems. We are mainly interested in simulating and modeling different systems, such as stochastic systems, dynamical systems, discrete-event systems, and complex network systems. This involves investigating modeling and simulation methodologies, statistical analysis, random number generators and their validations, Markov chains and Monte-Carlo methods, queue theory and graph theory. All the simulation and the modeling methods used in this course are programed using Python language programing.

1. Course Content :

   1. Introduction and General Methodologies to Modeling and Simulation (2h)

   2. Introduction to Scientific Programming with Python3 (2h)

   3. Simulation Probabilities and Random Number Generators (2.5h)

   4. Monte-Carlo Simulation Technique (2.5h)

   5. Markov-Chain Simulation techniques (2h).

   6. Introduction to Cellular Automata Simulation Techniques (2h)

   7. Modeling and Simulating Dynamical Systems (2.5h)

   8. Introduction to Queueing theory and Queuing Networks (2h)

   9. Modeling and Simulating Discrete-Event Systems (2h)

   10. Modeling and Simulating Complex Networks (2.5h)

2. List of Projects: [Team projects]

   1. UEFA Competition Draw

   2. Medical Clinic Queues Simulation

   3. Bank Queues Simulation

   4. Weather Forecasting Simulation

   5. Fluid dynamics Simulation

   6. Skin Animal simulation

3. Lab Frameworks :

   1. Python

   2. Networkx

   3. SciPy

   4. Simpy

Consultez les ressources disponibles concernant ce module sur le moteur de recherche de la bibliothèque, ou accédez directement au cours de vos enseignants via la plateforme de téléenseignement de l’école « e-learn ».