Physique statistique

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 basic concepts underlying computer simulations in classical and quantum problems, and connect these ideas to relevant and contemporary research topics in various fields of physics. In the TD’s you will also learn how to set, perform and analyse the results of simple computer simulations by yourself, covering a wide range of topics. We will use Python, but no previous knowledge of this programming language is needed.

Can the whole not merely be the sum of its parts? How do collective patterns appear?
Could three molecules of water form ice?
Could higher-level abilities be created from interacting AI agents?

The notion of complexity pertains to systems in which somewhat unexpected properties emerge from the interplay of a sufficiently large number of  entities — be they particles, living cells, artificial neurons, organisms, people, abstract agents... or even a mixture of some (or all!) of these.

Statistical physics has been the first branch of science to try and model in a mathematical manner such systems, focusing especially on the subtle and often elusive passage from the micro/individual level to the macro/collective level. This lecture course explores further how the mindset of statistical physics can provide fertile ground for the analysis and modelling of complexity, across disciplinary boundaries.

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.

Phase transitions take place in many different branches of physics: from soft and hard condensed matter to cosmology and high-energy physics. This course presents the fundamental ideas, concepts and methods that underpin the modern theory of phase transitions.