First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
MATHEMATICAL ENGINEERING
Course unit
MODEL IDENTIFICATION, CALIBRATION AND DATA ANALYSIS
INP5070359, A.A. 2017/18

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course Second cycle degree in
MATHEMATICAL ENGINEERING (Ord. 2017)
IN2191, Degree course structure A.Y. 2017/18, A.Y. 2017/18
N0
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Degree course track Common track
Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination MODEL IDENTIFICATION, CALIBRATION AND DATA ANALYSIS
Department of reference Department of Civil, Environmental and Architectural Engineering
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge GIORGIO PICCI ING-INF/04

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses ING-INF/04 Automatics 9.0

Mode of delivery (when and how)
Period Second semester
Year 1st Year
Teaching method frontal

Organisation of didactics
Type of hours Credits Hours of
teaching
Hours of
Individual study
Shifts
Lecture 9.0 72 153.0 No turn

Calendar
Start of activities 26/02/2018
End of activities 01/06/2018

Examination board
Board From To Members of the board
2 2016 01/10/2016 15/03/2018 PICCI GIORGIO (Presidente)
CALLEGARO GIORGIA (Membro Effettivo)
PINZONI STEFANO (Supplente)

Syllabus
Prerequisites: None
Target skills and knowledge: Objective
Introduce the students to the advanced topics of linear algebra and model identification.
Outcomes
A student who has met the objectives of the course will have a fundamental knowledge of :
• Linear algebra and numerical methods for large sparse matrices
• Deterministic and stochastic methods for model identification and calibration
Course unit contents: 1. Review of linear algebra concepts;
2. Iterative methods for the solution of large, sparse linear systems: a) conjugate gradient methods for symmetric systems; b) projection methods for nonsymmetric systems (GMRES-BiCGSTAB); c) preconditioning; incomplete factorizations; sparse factorized approximate inverses; d) implementation techniques; sparse (CSR) matrix storage;
3. Methods for the calculation of eigenvalues and eigenvectors: a) Power and inverse power (with shift) methods; b) QR method.
4. Newton methods for nonlinear systems: a) derivation of the Newton methods; b) local convergence properties and introduction to globalization techniques; c) Picard method; d) implementation of the Newton-Krylov and inexact Newton methods.
5. The calibration as an ill posed problem;
6. Penalizing functions;
7. Likelihood method for estimation;
8. Generalized Method of Moments;
9. Deterministic and stochastic algorithms.
Textbooks (and optional supplementary readings)