ID:
510502
Durata (ore):
52
CFU:
6
SSD:
FISICA TEORICA, MODELLI E METODI MATEMATICI
Anno:
2024
Dati Generali
Periodo di attività
Primo Semestre (23/09/2024 - 10/01/2025)
Syllabus
Obiettivi Formativi
The course covers some key aspects of statistical mechanics in their relation to probability theory, theoretical computers science and machine learning, and focuses on interdisciplinary applications of statistical physics in these areas, as well as on classical theories of liquids leveraging artificial intelligence methods. Rather than the systematic learning of an extended corpus, the objective is the development of a synthetic view of the subject. The tools provided aim at making students autonomous in their ability to explore contemporary issues in statistical mechanics, especially in their interdisciplinary applications, also through the use of active learning open-ended exercises, and reading-group experiences.
The student should ideally achieve:
• a “problem solving” approach through the exercises carried out in person and in groups.
• a sound and logical understanding of statistical mechanics through the topics presented in the course, as well as the the ability to place these topics in a broader context.
• the ability to explore independently a new topic in this area using text- books and scientific articles.
The student should ideally achieve:
• a “problem solving” approach through the exercises carried out in person and in groups.
• a sound and logical understanding of statistical mechanics through the topics presented in the course, as well as the the ability to place these topics in a broader context.
• the ability to explore independently a new topic in this area using text- books and scientific articles.
Prerequisiti
Basic knwoledge of Statistical Mechanics at undergraduate level is required. However, in view of the disparate backgrounds of prospected students, the course will strive to be self-contained.
Metodi didattici
Besides traditional lectures and exercise classes, students will be involved in tailored seminar activities, much in the spirit of an advanced reading course.
Verifica Apprendimento
The final exam is an oral test, based on an individual project, which consists in reproducing theoretically and computationally a preassigned topic. The oral examination is divided into two parts
1. Formal presentation (using blackboard and/or slides) of the assigned project, clearly explaining the question and its motivations, the methods employed, and the results achieved.
2. Questions, free discussion and deepening of the points that emerged during the presentation.
1. Formal presentation (using blackboard and/or slides) of the assigned project, clearly explaining the question and its motivations, the methods employed, and the results achieved.
2. Questions, free discussion and deepening of the points that emerged during the presentation.
Testi
1. Luca Peliti, Statistical Mechanics in a Nutshell (Princeton University Press 2009)
2. James Sethna, Statistical Mechanics: Entropy, Order Parameters, and Complexity (Oxford, 1st ed. 2006, 2nd ed. 2021)
3. Hidetoshi Nishimori, Statistical Physics of Spin Glasses and Information Processing: An Introduction (Clarendon Press 2001)
4. Andrés Santos, A Concise Course on the Theory of Classical Liquids: Basics and Selected Topics (Springer 2016)
5. Jean-Pierre Hansen and Ian R. McDonald, Theory of Simple Fluids with Applications to Soft Matter (London Academic Press 4th ed. 2013)
2. James Sethna, Statistical Mechanics: Entropy, Order Parameters, and Complexity (Oxford, 1st ed. 2006, 2nd ed. 2021)
3. Hidetoshi Nishimori, Statistical Physics of Spin Glasses and Information Processing: An Introduction (Clarendon Press 2001)
4. Andrés Santos, A Concise Course on the Theory of Classical Liquids: Basics and Selected Topics (Springer 2016)
5. Jean-Pierre Hansen and Ian R. McDonald, Theory of Simple Fluids with Applications to Soft Matter (London Academic Press 4th ed. 2013)
Contenuti
The course content can be divided into two parts.
(i) A set of central topics in the statistical mechanics of complex systems, including the relationships between statistical mechanics and probabil- ity theory, the understanding and use of mean-field methods, and the statistical physics of disordered systems and combinatorial-optimization problems. This part of the course is presented with a mixture of lectures, active study material, and exercises.
(ii) A selected collection of exemplary topics from the the modern theory of classical liquids, using the hard-sphere fluid ad a guiding tool to keep the course’s development directly accessible and computationally affordable. The scope of the course will range from Mayer’s systematic virial expan- sions to the analytic solution of Percus-Yevick’s equation. A number of topics will be treated in the style of a reading course, with an active in- volvement of students, also in view of developing the skills required for the final test.
(i) A set of central topics in the statistical mechanics of complex systems, including the relationships between statistical mechanics and probabil- ity theory, the understanding and use of mean-field methods, and the statistical physics of disordered systems and combinatorial-optimization problems. This part of the course is presented with a mixture of lectures, active study material, and exercises.
(ii) A selected collection of exemplary topics from the the modern theory of classical liquids, using the hard-sphere fluid ad a guiding tool to keep the course’s development directly accessible and computationally affordable. The scope of the course will range from Mayer’s systematic virial expan- sions to the analytic solution of Percus-Yevick’s equation. A number of topics will be treated in the style of a reading course, with an active in- volvement of students, also in view of developing the skills required for the final test.
Lingua Insegnamento
INGLESE
Corsi
Corsi
ARTIFICIAL INTELLIGENCE FOR SCIENCE AND TECHNOLOGY
Laurea Magistrale
2 anni
No Results Found
Persone
Persone
No Results Found