Risorse bibliografiche
Risorsa bibliografica obbligatoria
Risorsa bibliografica facoltativa
Scheda Riassuntiva
Anno Accademico 2021/2022
Tipo incarico Dottorato
Docente Berizzi Alberto
Cfu 5.00 Tipo insegnamento Monodisciplinare

Corso di Dottorato Da (compreso) A (escluso) Insegnamento

Programma dettagliato e risultati di apprendimento attesi

Data-driven dynamical systems is a new interesting field connected to modelling nonlinear systems starting from
measurements coming from the field.
The course will describe the most recent advances on numerical techniques able to make use of a set of
measurements from the fiels to extract a dynamic model of a physical process. The models then can be used both
to predict the behaviour of the system in the near future and to provide indications to control it for the best.
This is a critically important new direction, because the governing equations of many problems under
consideration by practitioners in various scientific fields are typically not known. Hence, using data to help derive,
in an optimal sense, the best dynamical system representation of a given process allows for important new
insights for both the analysis and the control of the system.
At the end of the course, the students will be able to use the proposed techniques in their own engineering field
to synthetise dynamic models of the studied phenomena, starting from a reasonably large set of measurements
coming from the field.


Note Sulla Modalità di valutazione

Project to be carried out at the end of the course.

Intervallo di svolgimento dell'attività didattica
Data inizio
Data termine

Calendario testuale dell'attività didattica

The course will deal with the following main topics:
Singular Value Decomposition (SVD) & Principal Components (PCA)
Overview of Supervised & Unsupervised Learning
Dynamic Mode Decomposition (DMD) & Koopman Theory
SINDy: Sparse ldentication of Nonlinear Dynamics
Partial differential equation discovery
Data Assimilation Methods & Kalman Filters
Neural Networks Overview
Gradient Descent & Backpropagation
Neural Networks for Dynamics
Autoencoders and Dynamics
Optimal Sensor Placement
Sparse Measurements and Cost Constraints

Risorsa bibliografica facoltativaJ.Nathan Kutz, Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data, Editore: Oxford University Press, ISBN: 978-0199660346

Software utilizzato
Nessun software richiesto

Mix Forme Didattiche
Tipo Forma Didattica Ore didattiche
laboratorio informatico
laboratorio sperimentale
laboratorio di progetto

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese

Note Docente
The course will be taught (25 hours) by prof. Prof. Nathan J. Kutz, University of Washington Professor Kutz was awarded the B.S. in Physics and Mathematics from the University of Washington in 1990 and the PhD in Applied Mathematics from Northwestern University in 1994. Following postdoctoral fellowships at the Institute for Mathematics and its Applications (University of Minnesota, 1994-1995) and Princeton University (1995-1997), he joined the faculty of applied mathematics and served as Chair from 2007-2015. His main research interests are related to numerical methods and scientific computing, data analysis and dimensionality reduction (PCA, POD, etc) methods, dynamical systems, bifurcation theory, linear and nonlinear wave propagation, perturbation and asymptotic methods, nonlinear analysis, variational methods, soliton theory, nonlinear optics, mode-locked lasers, fluid dynamics, Bose-Einstein condensation, neuroscience, gesture recognition and video & image processing.
schedaincarico v. 1.7.2 / 1.7.2
Area Servizi ICT