Admission Requirements
Bachelor in Physics and knowledge of basic statistical mechanics.
Description
What do a magnet, a Bose-Einstein condensate and a flock of birds have in common? All these systems exhibit a collective behavior and have large-scale physical properties that cannot be understood in terms of a simple extrapolation of the properties of a few particles. Conversely, systems comprising many interacting subunits often present entirely new properties, that scientists refer to as emergent.
Statistical Physics B, is the second part of a two-part introductory course on emergent phenomena in equilibrium and non-equilibrium systems. The course provides an introduction to phase transition and collective behavior in non-equilibrium systems, with special attention for active (i.e. self-driven) particles. The first (compulsory) part of this course is given in Statistical Physics A and is focused on phase transitions and critical phenomena at equilibrium.
The course consists of 4 lectures and 1 tutorial. During the tutorial, the students will work in groups and use interactive software (developed by former MSc student Leandros Talman) to simulate the dynamics of bird flocks.
Topics
Introduction to collective behavior: flocking, schooling, swarming etc.
The XY-model and the Mermin-Wagner theorem.
The Vicsek model.
Giant density fluctuations.
Course Objectives
The aim of the course is to develop a strong foundation in advanced statistical mechanics with an emphasis on emergent phenomena. Furthermore, the course aims to provide the students with a toolbox of mathematical techniques that can be readily used in theoretical and experimental research projects.
Specifically, at the end of the course, successful students will have learned how to:
Model the relaxation dynamics of equilibrating fields.
Construct a simple phenomenological hydrodynamic theory of self-propelled objects.
Calculate number density and order parameter fluctuations from hydrodynamic equations instead of the Hamiltonian.
Perform simple numerical simulations and analyze data (but no coding will necessary).
Transferable skills
At the end of the course, students will have been trained how to:
Work in teams.
Write a scientific essay based on original results.
Timetable
Physics Schedule
For detailed information go to Timetable in Brightspace
Mode of instruction
Zie Brightspace
Assessment method
Take-home exam consisting of a an analytical and a computational exercise.
Brightspace
Registration for Brightspace occurs via uSis
How to sign up for classes click here
Reading list
Reading material (research papers and notes) will be provided during the lectures.
Contact
Lecturer: Dr. Luca Giomi