Studying at the University of Verona

Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.

Study Plan

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.

1° Year

ModulesCreditsTAFSSD
9
B
SECS-P/08
12
C
MAT/06
12
B
INF/01

2° Year  activated in the A.Y. 2023/2024

ModulesCreditsTAFSSD
9
B/C
IUS/01 ,M-FIL/03
6
F
-
22
E
-
activated in the A.Y. 2023/2024
ModulesCreditsTAFSSD
9
B/C
IUS/01 ,M-FIL/03
6
F
-
22
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
1 module among the following (a.a. 2023/24: Data protection in business organizations not activated)
6
C
M-FIL/02
6
C
IUS/02
6
C
IUS/17
6
C
SPS/07
6
C
IUS/04
Between the years: 1°- 2°
2 modules among the following (a.a. 2023/24: Statistical methods for business intelligence not activated)
6
B
SECS-P/10
6
B
SECS-P/08
6
B
SECS-P/10
6
B
SECS-P/08
6
B
SECS-S/01
Between the years: 1°- 2°
2 modules among the following (a.a. 2023/24: Complex systems and social physics not activated)
6
C
FIS/02
6
C
MAT/09
6
C
AGR/01
6
C
MAT/06
6
C
MAT/08
6
C
FIS/02
Between the years: 1°- 2°
2 modules among the following
6
B
INF/01
6
B
ING-INF/05
6
B
ING-INF/05
6
B
INF/01
6
B
INF/01
Between the years: 1°- 2°
8
D
-

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.

A Basic activities
B Characterizing activities
C Related or complementary activities
D Activities to be chosen by the student
E Final examination
F Other training activities
S Placements in companies, public or private institutions and professional associations

Complex systems and social physics  (2022/2023)

Teaching code

4S009082

Academic staff

Gabriele Perugini, Fabrizio Illuminati

Coordinator

Gabriele Perugini

Credits

6

Also offered in courses:

  • Complex systems of the course Master's degree in Artificial intelligence

Language

English en

Scientific Disciplinary Sector (SSD)

FIS/02 - THEORETICAL PHYSICS, MATHEMATICAL MODELS AND METHODS

Period

Semester 2 dal Mar 6, 2023 al Jun 16, 2023.

Lessons timetable MoodleMoodle Seminars 0

Learning objectives

The aim of the course is to provide the student with the skills of theoretical physics and mathematical physics methods for the modeling and characterization of large sets of data, time series, time sequences, and hierarchical structures in aggregation. Students will also be provided with the methods of mathematical physics for the study of correlation, causation, and aggregation relationships in complex social systems. At the end of the course the student has to show to have acquired the ability to develop formal models for the qualitative and quantitative analysis of databases, time series, and dynamics of complex systems in interaction for the detection of causal relationships, correlation structures, and forecasting schemes.

Prerequisites and basic notions

Basic knowledge of calculus 1 (functions and ordinary differential equations) and calculus 2 (multiple variables functions, partial derivatives). Basic knowledge of probability and statistics. Some knowledge of classical mechanics is not necessary but can be helpful.

Program

Recap of ordinary differential equations (ODE). Definition of dynamical systems. Introduction to Chaos. Numerical solutions of ODE. Linear dynamical systems. Non-linear dinamical systems. Fixed points stability. Maps. Stability of maps. The Logistic map. The Lorenz model. Lotka-Volterra systems.

Introduction to stochastic processes. Stochastic population dynamics and biological evolution. Spatial systems. Reaction-diffusion processes. Non-stationary stochastic dynamics. Fokker-Plank equation. Anomalous diffusion. Traffic congestion models. Dynamical model of wealth repartition.

Introduction to statistical mechanics. Recal of Thermodynamics. Entropy. Statistical ensembles: isolated systems, microcanonical ensemble, canonical ensemble. Equilibrium statistical mechanics. Partition function. Introduction to phase transitions. Introduction to the Ising model: mean field approximation, 1d and 2d case.

Bibliography

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Didactic methods

Classroom lessons.

Learning assessment procedures

To pass the exam, the student must demonstrate: - have understood the principles underlying theoretical physics and mathematical physics for the modeling and characterization of statistical mechanics systems - be able to present their arguments in a precise and organic way without digressions - know how to apply the acquired knowledge to solve application problems presented in the form of exercises, questions and projects.
The final exam will include the writing of an essay on one or more topics covered in the course, with subsequent presentation and discussion.
Oral exam, possibly including the discussion of an essay on a topic covered in the course.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Evaluation criteria

Up to 30 points cum laude will be assigned to the oral exam.

Criteria for the composition of the final grade

The final grade will depend on the oral exam only.

Exam language

English