Statistical Physics

Recent years have seen enormous experimental progress in preparing, controlling and probing quantum systems in various regimes far from thermal equilibrium. Examples include systems as ultra-cold atomic quantum gases under time-dependent perturbations, driven non-linear cavity QED systems or strongly correlated electrons in solid-state materials under ultra-fast optical excitations.

Modern physics is characterized by an increasing complexity of systems under investigation, in domains as diverse as condensed matter, astrophysics, biophysics, etc. Due to the growing availability of experimental data, data-driven modelling is emerging as a powerful way to model those systems. The objective of the course is to provide the theoretical concepts and practical tools necessary to understand and to use these approaches.

Since the 80’s, laser cooling has enabled the production of sub-milliKelvin dilute atomic gases - which can be further cooled to the nanoKelvin regime.

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

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 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.