Statistical Physics

Computational physics plays a central role in all fields of physics, from classical statistical physics, soft matter problems, and hard-condensed matter. Our goal is to cover the very basic concepts underlying computer simulations in classical and quantum problems, and connect these ideas to relevant contemporary research problems in various fields of physics. In the TD’s you will also learn how to set, perform and analyse simple computer simulations by yourself. We will use Python, but no previous knowledge of this programming language is needed.

This course is an introduction to geometrical critical phenomena and their description by means of algebraic, probabilistic and quantum field theoretical techniques. 

The first aim of these lectures will be to give a brief overview of the physical and dynamical mechanisms which determine Earth’s climate. We will start with the atmospheric radiative transfer and the energy fluxes provided by the fluid dynamics of the atmosphere and the oceans.

Many physical systems exhibit phase transitions, i.e. abrupt changes in their properties when a parameter crosses a threshold value: a fluid changes from a liquid to a gaseous state at the evaporation temperature, a magnet loses its magnetic properties at the Curie temperature, and so on.

This course covers advanced topics in Statistical Physics. It assumes a very good knowledge of the Statistical Physics concepts and methods taught in standard lectures at the M1 level.

In this course, you will learn tools and ideas developed by statistical physics to deal with "complex systems". These tools can be used in different contexts, including economics and social sciences where the modelling of collective phenomena, crises, panics, and discontinuities, is more necessary than ever.