Statistical Physics of Complex Systems
A.Y. 2024/2025
Learning objectives
The course will deal with subjects of modern statistical mechanics useful in data science, like mean-field methods, disordered systems and spin glasses, problems of combinatorial optimisation, statistical mechanics of networks, inference.
Expected learning outcomes
The student will learn to use in an independent way the tools of interdisciplinary statistical mechanics, with application to biology, computer science, ecology, etc.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
The course content can be divided into
(i) a core of central arguments, which includes the relationships between statistical mechanics and probability theory, the understanding and use of mean-field methods, and the out-of-equilibrium dynamics and complex systems,
(ii) a set of modules that present varied models and fields of application (and can change year by year, even at the request of students)
A more detailed description of the course content is available on the course slack space
(i) a core of central arguments, which includes the relationships between statistical mechanics and probability theory, the understanding and use of mean-field methods, and the out-of-equilibrium dynamics and complex systems,
(ii) a set of modules that present varied models and fields of application (and can change year by year, even at the request of students)
A more detailed description of the course content is available on the course slack space
Prerequisites for admission
A previous knowledge of elementary Statistical Physics is useful.
Teaching methods
Frontal interactive lectures / exercises (it is recommended to attend)
Teaching Resources
The course is not based on any specific book but it is recommended to explore several books, among which the following:
Luca Peliti - Appunti di Meccanica Statistica / Statistical Mechanics in a Nutshell (Princeton University Press 2009)
Wolfgang Paul, Jorg Baschnagel. Stochastic Processes From Physics to Finance. Springer 2013.
Hidetoshi Nishimori. Statistical Physics of Spin Glasses and Information Processing: An Introduction, Clarendon Press 2001
L. Leuzzi, V Marinari e G Parisi, Calcolo delle Probabilità, Zanichelli 2023
James Sethna, Statistical Mechanics: Entropy, Order Parameters, and Complexity (Oxford, 1st ed. 2006, 2nd ed. 2021)
See the Syllabus document on the course slack space: statmech2.slack.com for further details
Luca Peliti - Appunti di Meccanica Statistica / Statistical Mechanics in a Nutshell (Princeton University Press 2009)
Wolfgang Paul, Jorg Baschnagel. Stochastic Processes From Physics to Finance. Springer 2013.
Hidetoshi Nishimori. Statistical Physics of Spin Glasses and Information Processing: An Introduction, Clarendon Press 2001
L. Leuzzi, V Marinari e G Parisi, Calcolo delle Probabilità, Zanichelli 2023
James Sethna, Statistical Mechanics: Entropy, Order Parameters, and Complexity (Oxford, 1st ed. 2006, 2nd ed. 2021)
See the Syllabus document on the course slack space: statmech2.slack.com for further details
Assessment methods and Criteria
The exam consists of an oral discussion that focuses on the topics covered during the course, addressing the ability of the student to elaborate a synthetic view of the subject and to explore independently a topic of choice.
FIS/02 - THEORETICAL PHYSICS, MATHEMATICAL MODELS AND METHODS - University credits: 6
Lessons: 42 hours
Professor:
Cosentino Lagomarsino Marco
Professor(s)