Servizi per la didattica
PORTALE DELLA DIDATTICA

Surrogate modeling: theory for the user

01PKCRV

A.A. 2018/19

Course Language

Inglese

Course degree

Doctorate Research in Ingegneria Elettrica, Elettronica E Delle Comunicazioni - Torino

Course structure
Teaching Hours
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
*** N/A ***    
PERIODO: OTTOBRE 2016 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
PERIODO: OTTOBRE 2016 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,...)
* 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,...)
Gli studenti e le studentesse con disabilitÓ o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'UnitÓ Special Needs, al fine di permettere al/la docente la declinazione pi¨ idonea in riferimento alla specifica tipologia di esame.
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.
Esporta Word


© Politecnico di Torino
Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY
Contatti