Academic Year 2020/2021 - 1° Year
Teaching Staff: Vittorio Romano
Credit Value: 6
Scientific field: MAT/07 - Mathematical physics
Taught classes: 28 hours
Exercise: 30 hours
Term / Semester:

Learning Objectives

The course aims to provide the basic knowledge of numerical analysis and probability, as well as

introductory elements of statistical problems. The teaching method of the course consists of lectures,

programming elements in Matlab or similar language and computer exercises.

In particular, the course aims to allow the student to acquire the following skills:

knowledge and understanding: knowledge of results and fundamental methods in numrical analysis, probability and statistics. Skill of understanding problems and to extract the major features. Skill of reading, undertanding and analyzing a subject in the related literature and present it in a clear and accurate way.

applying knowledge and understanding: skill of elaborainge new example or solving novel theoretical exsercise, looking for the most appopriate methods and applying them in an appropriate way.

making judgements: To be able of devise proposals suited to correctly interprete complex problems in the framework of numerics, probablity, statistics and their applications. To be able to formulate autonomously adequate judgements on the applicablity of mathematical models to theoretical or real situations.

communication skills: skills of presenting arguments, problems, ideas and solutions in mathematical terms with clarity and accuracy and with procedures suited for the audience, both in an oral and a written form. Skill of clearly motivating the choice of the strategy, method and contents, along with the employed computational tools.

learning skills: reading and analyzing a subject in the engineering literature involving applied mathematics. To tackle in an autonomuous way the systematic study of arguments not previously treated. To acquire a degree of autonomy such that the student can be able to start with an autonomuos reserach activity.

Course Structure

Mainly frontal lectures. Moreover, the theoretical acquired competencies will be applied in a laboratory where study cases will be tackled in a MATLAB enviroment.

Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

Learning assessment may also be carried out on line, should the conditions require it.

Detailed Course Content

Introduction to programming in Matlab or similar language. Numbering systems. Linear systems. Zeros of

nonlinear equations. Methods of interpolation and approximation. Quadrature formulas. Numerical

differentiation. Numerical methods for ordinary differential equations. Introduction to partial differential

equations. Elements of probability and statistics.

Textbook Information

Main textbook: V. Romano, Metodi matematici per i corsi di ingegneria, CittàStudi edizioni

Further readings

G. Monegato, Cento pagine di … Elementi di Calcolo Numerico, Libreria Universitaria Levrotto e Bella, Torino

A. Quarteroni, R, Sacco, F. Saleri, Matematica Numerica, Springer

V. Comincioli, Analisi Numerica: metodi, modelli, applicazioni, McGraw-Hill

P. Baldi Calcolo delle probabilità e statistica, McGraw-Hill

R. Scozzafava Incertezza e probabilità, Zanichelli