Politecnico di Torino
Politecnico di Torino
Politecnico di Torino
Anno Accademico 2012/13
Surrogate modeling: theory for the user
Dottorato di ricerca in Ingegneria Elettronica E Delle Comunicazioni - Torino
Docente Qualifica Settore Lez Es Lab Tut Anni incarico
Stievano Igor Simone ORARIO RICEVIMENTO O2 ING-IND/31 20 0 0 0 4
SSD CFU Attivita' formative Ambiti disciplinari
*** N/A ***    
Obiettivi dell'insegnamento

Surrogate or black-box models are compact mathematical representations that attempt to mimic the behavior of a system without insights on its internal structure or physical governing equations. Surrogates find application in several engineering domains due to their ability to approximate the system behavior with good accuracy, starting from limited input-output information. Therefore, they offer an effective solution for carrying out fast numerical simulation in analysis and design flows (e.g., for exploring the design space, what-if, optimization and sensitivity analyses).
This course focuses on the presentation of the essential mathematical background and to the application of basic methods belonging to system identification for the generation of surrogate models of linear and nonlinear (possibly dynamical) systems. An engineering approach is adopted, with emphasis on the practical user aspects. The students are guided to build surrogate models via the application of ready-to-use Matlab templates and routines. The example problems selected in the practice sessions are simple enough to lower the technical barrier and to highlight the key modeling aspects, thus showing what the users can expect from the identification framework to solve their modeling problems.
* Introduction, classification and characteristics of a (dynamical) system and of the available modeling resources presented in the course
* Identification (overview of linear vector spaces, illustration of some important aspects of system identification, parameter estimation, selection of the model complexity, excitation signals,...)
* Identification of linear dynamical systems
* Identification of nonlinear dynamical systems
* Data exploration and processing via principal component analysis
* Surrogate modeling resources (books, toolboxes,...)
Orario delle lezioni
Statistiche superamento esami

Programma provvisorio per l'A.A.2012/13

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