- Docente: Serena Morigi
- Credits: 6
- SSD: MAT/08
- Language: English
- Teaching Mode: Traditional lectures
- Campus: Cesena
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Corso:
Second cycle degree programme (LM) in
Electronics and Telecommunications Engineering for Energy (cod. 8770)
Also valid for Second cycle degree programme (LM) in Biomedical Engineering (cod. 6705)
Second cycle degree programme (LM) in Biomedical Engineering (cod. 6705)
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from Sep 17, 2025 to Oct 28, 2025
Course contents
The first part cover a first course in Numerical Analysis. Cover the basic techniques of the subject and provides a foundation for the efficient numerical solution of problems in science and engineering. Numerical methods to solve linear and nonlinear systems , numerical optimization, numerical derivative, numerical integration, interpolation, approximation. A brief introduction to inverse problems and regularization techniques.
This second part of the course presents numerical methods for the solution of problems modeled by both Ordinary Differential Equations (ODE) and Partial Differential Equations (PDE). The course discusses their analysis, applications, and computation of the solution (by first discretizing the equation, bringing it into a finite-dimensional subspace by a finite element method, or a finite difference method , and finally reducing the problem to the solution of an algebraic equations)
Numerical Analysis (6 CFU)
1. Linear algebra: matrices, vector and matrix norm, eigendecomposition, matrix algebra.
2. Basics of numerical computing: floating-point arithmetic, roundoff errors, algorithms, problem conditioning, numerical stability.
3. Introduction to programming using MATLAB.
4. Solving Linear Systems. Direct methods: LU factorization, pivoting, Gaussian elimination, Cholesky factorization, Thomas algorithm. Solving Linear Systems. Itarative methods:Gauss-Seidel, Conjugated Gradients, Preconditioning.
5. Numerical solution of nonlinear equations and systems: bisection method, Newton's method, secant, regula falsi.
6. Polynomial approximation of data by least squares: normal equations, method based on QR factorization and SVD. Polynomial interpolation and piecewise polynomial interpolation
7. Regularization methods for ill-posed problems, Principal Component Analysis.
8. Numerical integration: Newton Cotes quadrature formulas of simple and composite. Numerical differentiation
9. Numerical Optimization: gradient descent, stochastic gradient descent, optimization for machine learning, Newton's method , Gauss-Newton method.
Numerical Methods for Differential Equations (3 CFU)
1. Numerical Solution of Ordinary Differential Equations: One step methods; Control of error; Definition of the step-size and adaptive-step methods; stability; Methods for Stiff Problems;
2. Boundary value problems;
3. Numerical Solution of Partial Differential Equations; Classification; domain of dependence, boundary conditions; Finite difference methods for parabolic problems; transport equation, hyperbolic problems: Galerkin method for Parabolic Problems; Elliptic equations: finite difference method and basics on finite element methods.
Readings/Bibliography
A First Course in Numerical Methods, Uri M. Ascher Chen Greif, SIAM
Cleve Moler, Numerical Computing with MATLAB , Ed. SIAM, 2004.
Michael T. Heath, Scientific Computing: An Introductory Survey , 2nd ed., McGraw-Hill, 2002.
A.Quarteroni, F.Saleri, P.Gervasio, Scientific Computing with MATLAB and Octave, 2010
A. Quarteroni, Numerical Models for Differential Problems, 2014, ISBN 978-88-470-5522-3
Randall J. Leveque, Finite Difference Methods for Ordinary and Partial Differential Equations: Steady-State and Time-Dependent Problems, 2007
Teaching methods
class hours and computational exercises in lab.
Assessment methods
The exam consists of a written parts and an oral one:
Written exam (2h30) for NUMERICAL ANALYSIS
Oral exam for the second part Differential Equations.
An individual project is assigned at the end of the course for part B. It consists in a biomedical problem with a ODE/BVP/PDE formulation involved to be solved in MATLAB. A report on the proposed implementation and experiments has to be submitted before the oral part.
Teaching tools
Experience in Lab. is an essential part of the course. Matlab is used as problem solving environment, matrix-vector programming language, graphics.
Slides provided in the WEB site in the Platform virtuale.unibo.itOffice hours
See the website of Serena Morigi