First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
PHYSICS OF DATA
Course unit
STATISTICAL MECHANICS OF COMPLEX SYSTEMS
SCP8082536, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
PHYSICS OF DATA
SC2443, Degree course structure A.Y. 2018/19, A.Y. 2018/19
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination STATISTICAL MECHANICS OF COMPLEX SYSTEMS
Website of the academic structure http://physicsofdata.scienze.unipd.it/2018/laurea_magistrale
Department of reference Department of Physics and Astronomy
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge SAMIR SIMON SUWEIS FIS/03

Mutuated
Course unit code Course unit name Teacher in charge Degree course code
INP5070380 STATISTICAL MECHANICS OF COMPLEX SYSTEMS SAMIR SIMON SUWEIS IN2371
INP5070380 STATISTICAL MECHANICS OF COMPLEX SYSTEMS SAMIR SIMON SUWEIS IN2371

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses FIS/03 Material Physics 6.0

Course unit organization
Period Second semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 6.0 48 102.0 No turn

Calendar
Start of activities 25/02/2019
End of activities 14/06/2019

Examination board
Examination board not defined

Syllabus
Prerequisites: Stochastic processes: Brownian motion, Langevin equation, Master and Fokker-Planck equations. Thermodynamics of phase transitions.
Critical points, order parameters and critical exponents. Finite size scaling.
Ising model and mean field theories.
Target skills and knowledge: After completing the course the student should be able to understand and explain the basic concepts and the use of advanced techniques in statistical mechanics of complex systems..
In particular, the student will
1) Acquire the ability to build an appropriate phenomenological theoretical model based on the available data of the system
2) Give an account of the relevant and minimal amount of quantities needed to describe the system (use of null model).
3) Understand the use of generating functions.
4) Explain the concept of phase transitions in out of equilibrium interacting particle models as well as the physics at or near critical points.
5) Understand the strength and limitation of the models
6) Show an analytic ability to solve problems relevant to complex systems
Examination methods: The first part of the verification of the acquired knowledge will evaluated be through homework exercises (to do in groups) and the participation of the students in the class discussions The second part will takes place through, a common written test with 1-2 exercises to be solved and open questions to test the knowledge on basic concepts, the scientific vocabulary, the ability to synthesis and critical discussion acquired during the course. The third facultative part of the exam will be oral and will be based on a discussion on the various topics discussed during the course.
Assessment criteria: The criteria used to verify the knowledge and skills acquired are:
1) understanding of the topics covered;
2) critical ability to connect the acquired knowledge;
3) completeness of the acquired knowledge;
4) synthesis ability;
5) understanding of the terminology used
6) ability to use the analytical methodologies and computational techniques illustrated during the course to solve or at least to approach set problems on complex systems where statistical mechanics plays an important role.
Course unit contents: The program can be summarized as follow
Complex networks: basic measures and statistics. Real networks and their property.
Null models and random graphs. Generating function formalism.
Cluster size and percolation on networks; phase transitions.
Dynamics of and on networks
Interacting particle models: voter model and contact process.
Gillespie algorithm, Master Equations and mean field.
Application to ecology, epidemics and neuroscience.

Please note that some topics may vary.
Planned learning activities and teaching methods: The course is organized in lectures whose contents are presented on the blackboard, sometimes with the help of images, diagrams and videos. The teaching is interactive, with questions and presentation of case studies, in order to promote discussion and critical thinking in the classroom.
Additional notes about suggested reading: Beyond some suggested books, materials (notes and published papers) will be available to the students in Moodle.
Textbooks (and optional supplementary readings)
  • Newman, Mark E. J., Networksan introductionM. E. J. Newman. Oxford: New York, Oxford University Press, 2010. Cerca nel catalogo
  • Krapivsky, Pavel L.; Redner, Sidney, <<A >>kinetic view of statistical physicsPavel L. Krapivsky, Sidney Redner, Eli Ben-Naim. Cambridge: University press, --. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Problem based learning
  • Interactive lecturing
  • Working in group
  • Video shooting made by the teacher/the students
  • Use of online videos
  • Loading of files and pages (web pages, Moodle, ...)
  • Learning journal

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • One Note (digital ink)
  • Mathematica

Sustainable Development Goals (SDGs)
Quality Education Industry, Innovation and Infrastructure Life on Land