NUMERICAL METHODS IN ENGINEERING SCIENCES
Stampa
Anno immatricolazione
2020/2021
Anno offerta
2020/2021
Normativa
DM270
SSD
MAT/08 (ANALISI NUMERICA)
Dipartimento
DIPARTIMENTO DI INGEGNERIA INDUSTRIALE E DELL'INFORMAZIONE
Corso di studio
INGEGNERIA ELETTRICA
Curriculum
PERCORSO COMUNE
Anno di corso
Periodo didattico
Primo Semestre (28/09/2020 - 22/01/2021)
Crediti
6
Ore
46 ore di attività frontale
Lingua insegnamento
English
Tipo esame
SCRITTO E ORALE CONGIUNTI
Docente
SANGALLI GIANCARLO (titolare) - 6 CFU
Prerequisiti
Differential and integral calculus for real functions; complex numbers; linear algebra; computer programming experience.
Obiettivi formativi
The aim of the course is to enable students to classify real-life problems and choose the best suited algorithms for solving them, in terms of costs/benefits and convergence properties. At the same time, the course is meant to make students well acquainted with the use of Matlab software and with the practical implementation of some algorithms.
Programma e contenuti
NUMERICAL SOLUTION OF BOUNDARY VALUE PROBLEMS FOR PARTIAL DIFFERENTIAL EQUATIONS (PDE):

* Finite Difference method on a model problem in 1D. Consistency and Stability - Lax's Theorem for convergence of a numerical scheme.
*Finite Element method on a model problem in 1D: Variational formulation, continuous piecewise linear finite element approximation, stability and convergence; construction of the final system and comparison with finite differences.
*Finite Element method on a model problem in 2D: Variational Formulation, Continuous piecewise linear finite element discretization on triangular meshes; Explicit computation of the elementary stiffness matrix and right-hand side; Assembling and solution of the final system.
*Various examples of boundary value problems in 2D.

NUMERICAL SOLUTION OF INITIAL VALUE PROBLEMS FOR ORDINARY DIFFERENTIAL EQUATIONS (ODE):

*One-step methods: Euler backward and forward, Crank-Nicolson, Heun; Stability and A-stability, consistency, convergence and order of convergence.
*Multistep Methods: general structure, consistency and stability conditions; Explicit and Implicit Adams methods.
*Runge-Kutta methods: consistency and stability conditions; example of construction of an explicit RK-method (Hints on predictor-corrector methods).
*Systems of Ordinary Differential Equations: stiff problems.

COMMON TOOLS:

*Solution of linear systems of equations: direct and iterative methods.
*Nonlinear equations: bisection and Newton's methods. Convergence, order of convergence, stopping criteria.
*Lagrange interpolation: interpolation error, piecewise Lagrange interpolation, order of approximation.
*Least squares method for data fitting: linear regression and various examples.
*Interpolatory quadrature formulas in 1D: midpoint, trapezoidal, Simpson and error analysis. Gaussian formulae.Extension to dimension 2 on rectangular domains. Quadrature formulas on triangular domains: barycenter, vertex, and midpoint of the edges.
Metodi didattici
Lectures (hours/year in lecture theatre): 45
Practical class (hours/year in lecture theatre): 0
Practicals / Workshops (hours/year in lecture theatre): 0
Testi di riferimento
A. Quarteroni, R. Sacco, F. Saleri . Numerical Mathematics-2nd edition. Springer Series: Texts in Applied Mathematics, Vol. 37 (2007).
Modalità verifica apprendimento
The exam will be written. Each student will be offered a couple of questions on subjects developed in the classes and has one hour to answer.
Altre informazioni
Additional information can be found on the web page:
http://www-dimat.unipv.it/sangalli
Obiettivi Agenda 2030 per lo sviluppo sostenibile