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

A.A. 2026/2027 A.A. 2025/2026

This information is intended exclusively for future freshmen who will enroll for the 2025/2026 academic year.
If you are already enrolled in this course of study, consult the information available on the course page:

Master's Degree in in Computer Engineering for Intelligent Systems - Enrollment until 2024/2025

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.

CURRICULUM TIPO:

1° Year 

ModulesCreditsTAFSSD
6
B
ING-INF/04
6
B
ING-INF/05
12
B
ING-INF/05
12
C
ING-INF/06

2° Year   It will be activated in the A.Y. 2026/2027

ModulesCreditsTAFSSD
Final exam
24
E
-
It will be activated in the A.Y. 2026/2027
ModulesCreditsTAFSSD
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
4 modules among:
- 1st year - Embedded operating systems, Embedded & IoT Systems design, Robotics, Computer vision, Advanced visual computing and 3D modeling - delivered in 2025/2026
- 2nd year - Advanced control systems - delivered in 2026/2027
6
B
ING-INF/04
6
B
ING-INF/05
6
B
ING-INF/05
6
B
ING-INF/05
6
B
ING-INF/04
6
B
ING-INF/05
Between the years: 1°- 2°
3 modules among:
- 2nd year -  Advanced methods for biomedical signal processing, Neurohealth, Medical robotics, Internet of Medical things - delivered in 2026/2027
- 1st or 2nd year - Mathematical modeling for Industrial and medical digital twins, Cloud computing and distributed systems - delivered in 2025/2026 or in 2026/2027
6
C
ING-INF/06
6
C
ING-INF/04 ,MED/50
6
C
ING-INF/06 ,MED/37
6
C
INF/01
6
C
INF/01 ,MED/50
6
C
MAT/07
Between the years: 1°- 2°
Further activities
6
F
-
Between the years: 1°- 2°
12
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

Mathematical modeling for industrial and medical digital twins (2025/2026)

Teaching code

4S012360

Credits

6

Coordinator

Nicola Sansonetto

Language

English en

Scientific Disciplinary Sector (SSD)

MAT/07 - MATHEMATICAL PHYSICS

Courses Single

Authorized

MoodleMoodle Seminars 0

The teaching is organized as follows:

NUMERICAL OPTIMIZATION
The activity is given by Numerical modelling and optimization - NUMERICAL OPTIMIZATION of the course: Master's degree in Mathematics

Credits

3

Period

2nd semester

Academic staff

Giacomo Albi

Lessons timetable
MODELLING SEMINAR
The activity is given by Numerical modelling and optimization - MODELLING SEMINAR of the course: Master's degree in Mathematics

Credits

3

Period

2nd semester

Academic staff

Nicola Sansonetto

Lessons timetable

Learning objectives

The aim of the first module is to deepen the knowledge and skills especially in the modern theory of dynamical systems and give the student a solid appreciation of the deep connections between mathematics and other scientific disciplines, both in terms of the mathematical problems that they inspire and the important role that mathematics plays in scientific research and industry. Mathematical software tools, and others, will be used to implement algorithms for the solution of the real world problems studied during the course. At the end of the course the student is expected to be able to complete professional and technical tasks of a high level in the context of mathematical modelling and computation, both working alone and in groups. In particular the student will be able to write a model of a real problem, to recognise the effective parameters and analyse the model and its possible implications. The second module wants to provide sufficient theoretical and numerical background for the optimal control of dynamical systems. Such problems will be developed by means of real application examples, and recent research studies. At the end of the course students will be able to decide which numerical method is suitable for the solution of some specific optimal control problems. He/She will be able to provide theoretical results on the controllability and stability of certain optimal control problem and numerical methods. He/She will be able to develop his/her own code, and capable choose the appropriate optimization method for each application shown during the course.

Prerequisites and basic notions

- Multivariate differential calculus, linear algebra, foundations of dynamical systems
- Scientific programming language (python/matlab/octave)

Program

MATHEMATICAL MODELLING
1. Review of Dynamical Systems: vector fields, flux, equilibria and their stability, periodic orbits, phase portraits, first integrals. Estimation of the separation of orbits. Translations of the circle. Numerical integrations and phase portraits for systems in 2 dimensions.
2. Bifurcations and limit cycles: bifurcations in 2 dimensions, limit cycles, Poincare-Bendixon theorem. Numerical investigations and applications.
3. Non-autonomous vector fields, extended phase space. Flow box and Poincare map. Discrete-time maps. Numerical integrations with applications.
4. Notes on strange attractors and chaotic systems.
5. Introduction to the Koopman operator.
6. Applications in physics, engineering, and life sciences.
NUMERICAL OPTIMIZATION
* Introduction to optimal control theory for linear and nonlinear problems.
* Numerical methods for optimal control: Direct and indirect methods, dynamic programming, and MPC.
* Continuous optimization: gradient, quasi-Newton, and Newton methods.
* Examples and exercises in Matlab.
* Applications in physics, engineering, and life sciences.

Bibliography

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Didactic methods

Lectures, group work, homework, weekly group reviews. Development of an individual or group project.

Learning assessment procedures

Students must be able to formalize and solve mathematical models used in various scientific disciplines, using, adapting, and developing the advanced methods discussed in class. To this end, the final assessment consists of:
- completing homework exercises to be submitted and discussed;
- a test for the Mathematical Modeling module: questions and exercises that may require the use of a calculator;
- a test for the Numerical Optimization module;
- developing a project in groups, on topics proposed by the instructor or by individual groups in consultation with the instructor. The project topic must be agreed upon with the instructor.

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

To pass the exam, students must demonstrate:
- knowledge and understanding of the fundamental concepts and techniques of numerical optimization and dynamic systems;
- adequate analytical, synthesis, abstraction, and computational skills;
- the ability to argue their arguments with mathematical rigor.

Criteria for the composition of the final grade

The final grade is therefore composed of three parts: two-thirds of the grade is the arithmetic mean of the grades for the first two parts. The final third is based on the project's drafting and presentation.

Exam language

English