01SPDPF

A.A. 2023/24

Course Language

Inglese

Course degree

Master of science-level of the Bologna process in Physics Of Complex Systems (Fisica Dei Sistemi Complessi) - Torino/Trieste/Parigi

Course structure

Teaching | Hours |
---|

Teachers

Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut | Years teaching |
---|

Teaching assistant

Context

SSD | CFU | Activities | Area context |
---|---|---|---|

FIS/02 | 6 | C - Affini o integrative | Attività formative affini o integrative |

2019/20

Module 1: Nonlinear Physics and dynamical systems (C. Nore, 3 ECTS)
Nonlinear systems are ubiquitous in real world. In this case the superposition principle is no more valid meaning that the response of the system is not proportional to the input it receives; the sum of different solutions is not a solution anymore. After a brief introduction, we study systems with increasing complexity starting from one dimensional systems to multiple dimension ones. As nonlinear equations are difficult to solve, nonlinear systems are commonly approximated by linear equations using linearization about a basic state. Multiple solutions can exist that are visited through bifurcations when a parameter of the system is varied and can lead to interesting phenomena such as chaos.
Module 2: Computational Science (F. Krzakala, 3 ECTS)
This lecture introduces the students to the use of computing tools, with applications ranging from statistical physics to complex systems and modern subjects such as machine learning. The course is accompanied by tutorials, and students are given homework to program by themselves. We also attempt to introduce the students to modern applied mathematics and statistics (frequentist and Bayesian) and neural networks, and seek a fined-tune balance between mathematics, physics, algorithms and programming.

Module 1: Nonlinear Physics and dynamical systems (C. Nore, 3 ECTS)
Nonlinear systems are ubiquitous in real world. In this case the superposition principle is no more valid meaning that the response of the system is not proportional to the input it receives; the sum of different solutions is not a solution anymore. After a brief introduction, we study systems with increasing complexity starting from one dimensional systems to multiple dimension ones. As nonlinear equations are difficult to solve, nonlinear systems are commonly approximated by linear equations using linearization about a basic state. Multiple solutions can exist that are visited through bifurcations when a parameter of the system is varied and can lead to interesting phenomena such as chaos.
Module 2: Computational Science (F. Krzakala, 3 ECTS)
This lecture introduces the students to the use of computing tools, with applications ranging from statistical physics to complex systems and modern subjects such as machine learning. The course is accompanied by tutorials, and students are given homework to program by themselves. We also attempt to introduce the students to modern applied mathematics and statistics (frequentist and Bayesian) and neural networks, and seek a fined-tune balance between mathematics, physics, algorithms and programming.

.

.

.

.

.

.

.

.

.

.

Module 1: Nonlinear Physics and dynamical systems
Nonlinear dynamics and chaos, S. Strogatz, Westview Press.
Structures dissipatives, chaos et turbulence, P. Manneville, Aléa Saclay.
Course Notes for Nonlinear Dynamics, L. Tuckerman, https://www.pmmh.espci.fr/~laurette/explanatory
Module 2: Computational Science
Statistical Mechanics: Algorithms and Computations, W. Krauth, Oxford University Press.
The Elements of Statistical Learning, T. Hastie, R. Tibshirani & J. Friedman, Springer.
All of statistics a concise course in statistical inference, L. Wasserman, Springer.

Module 1: Nonlinear Physics and dynamical systems
Nonlinear dynamics and chaos, S. Strogatz, Westview Press.
Structures dissipatives, chaos et turbulence, P. Manneville, Aléa Saclay.
Course Notes for Nonlinear Dynamics, L. Tuckerman, https://www.pmmh.espci.fr/~laurette/explanatory
Module 2: Computational Science
Statistical Mechanics: Algorithms and Computations, W. Krauth, Oxford University Press.
The Elements of Statistical Learning, T. Hastie, R. Tibshirani & J. Friedman, Springer.
All of statistics a concise course in statistical inference, L. Wasserman, Springer.

...
.

Gli studenti e le studentesse con disabilità 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'Unità Special Needs, al fine di permettere al/la docente la declinazione più 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.

© Politecnico di Torino

Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY

Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY