Enrollment year
2020/2021
Academic discipline
MAT/08 (NUMERICAL ANALYSIS)
Department
DEPARTMENT OF ELECTRICAL,COMPUTER AND BIOMEDICAL ENGINEERING
Course
ELECTRICAL ENGINEERING
Curriculum
PERCORSO COMUNE
Period
1st semester (28/09/2020 - 22/01/2021)
Lesson hours
46 lesson hours
Activity type
WRITTEN AND ORAL TEST
Prerequisites
Differential and integral calculus for real functions; complex numbers; linear algebra; computer programming experience.
Learning outcomes
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.
Course contents
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.
Teaching methods
Lectures (hours/year in lecture theatre): 45
Practical class (hours/year in lecture theatre): 0
Practicals / Workshops (hours/year in lecture theatre): 0
Reccomended or required readings
A. Quarteroni, R. Sacco, F. Saleri . Numerical Mathematics-2nd edition. Springer Series: Texts in Applied Mathematics, Vol. 37 (2007).
Assessment methods
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.
Further information
Additional information can be found on the web page:
http://www-dimat.unipv.it/sangalli
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