Course Unit Page

Academic Year 2017/2018

Learning outcomes

Basic knowledge of physical and mathematical methods to develop dynamic and statistical model for the study of complex systems.

Basic knowledge of graphical methods 2D and 3D used to illustrate the results.

Course contents

Complex system definition. Role of non-linear interactions.

Simple theoretical and numerical models for complex systems.

Examples from Physics, economy and biology.

Data distribution: comparison between exponential and power laws.

Agent, neural network and cellular automata models.

Numerical solution of ordinary differential equations (ODE) with errors evaluation. Numerical solution of partial differential equations (PDE) with errors evaluation. Consistency, stability and convergence. Second order linear PDE: wave, heat and Laplace equation.

Solution visualization and animation. Introduction to OpenGl.

Final C++ project with OpenGl graphics.


class reading;

Numerical Recipes, W.H.Press et al, Cambridge University Press

Teaching methods

lessons, seminar and home works.

Assessment methods

The final exam and/or the project report at the end of the course aims to assess the achievement of learning objectives:

- To know the methodologies of the discipline in particular on data analysis, on numerical simulations and on solution visualization.

- To understand the characteristics of the various physical and social systems to which the methodologies discussed can be applied.

Teaching tools

personal PC. videoprojector, internet

Office hours

See the website of Sandro Rambaldi