Università di Pavia - Offerta formativa

NUMERICAL METHODS IN ENGINEERING SCIENCES

Anno immatricolazione

2016/2017

Anno offerta

2016/2017

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

1°

Periodo didattico

Primo Semestre (26/09/2016 - 13/01/2017)

Crediti

6

Ore

45 ore di attività frontale

Lingua insegnamento

ENGLISH

Tipo esame

SCRITTO E ORALE CONGIUNTI

Docente

MARINI LUISA DONATELLA (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

The course is divided in two parts, devoted essentially to the numerical approximation of boundary value problems for Partial Differential Equations (Pde's), and of initial value problems for Ordinary Differential Equations (Ode's). The basic common and necessary instruments to deal with both classes of problems are also developed.

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. Nonlinear systems of equations: Newton's method and variants.

*Lagrange interpolation: interpolation error, piecewise Lagrange interpolation, order of approximation in various norms.

*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.

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. Nonlinear systems of equations: Newton's method and variants.

*Lagrange interpolation: interpolation error, piecewise Lagrange interpolation, order of approximation in various norms.

*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

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.

There are two typologies of exam:

Basic exam: it consists in a couple of simple questions and/or exercises, intended to verify the capability of applying the numerical algorithms, without the need for a deep understanding. The maximum grade is 24/30.

Advanced exam: it consists in a couple questions (more theoretical than in the basic exam), intended to verify comprehension of the subjects and not just a mere application of ready-to-use formulas. The answers must be articulated with a certain mathematical precision. The maximum grade is 30/30 cum laude.

There are two typologies of exam:

Basic exam: it consists in a couple of simple questions and/or exercises, intended to verify the capability of applying the numerical algorithms, without the need for a deep understanding. The maximum grade is 24/30.

Advanced exam: it consists in a couple questions (more theoretical than in the basic exam), intended to verify comprehension of the subjects and not just a mere application of ready-to-use formulas. The answers must be articulated with a certain mathematical precision. The maximum grade is 30/30 cum laude.

Altre informazioni

Additional information can be found on my web page:

http://arturo.imati.cnr.it/marini

http://arturo.imati.cnr.it/marini

Obiettivi Agenda 2030 per lo sviluppo sostenibile