讲座题目：网络系统的同步动力学
主讲人：Stefano Boccaletti 院士
主持人：管曙光 教授
开始时间：2024-06-07 10:00
讲座地址：闵行校区物理楼226
主办单位：物理与电子科学学院
报告人简介：
Stefano Boccaletti received the PhD in Physics at the University of Florence on 1995, and a PhD honoris causa at the University Rey Juan Carlos of Madrid on 2015. He was Scientific Attache’ of the Italian Embassy in Israel during the years 2007-2011 and 2014-2018. He is currently Director of Research at the Institute of Complex Systems of the Italian CNR, in Florence. His major scientific interests are i) pattern formation and competition in extended media, ii) control and synchronization of chaos, and iii) the structure and dynamics of complex networks. He is Editor in Chief of the Journal “Chaos, Solitons and Fractals” (Elsevier) from 2013, and member of the Academia Europaea since 2016. He was elected member of the Florence City Council from 1995 to 1999. Boccaletti has published 352 papers in peer-reviewed international Journals, which received more than 35,000 citations (Google Scholar). His h factor is 70 and his i-10 index is 227. With more than 12,300 citations, the monograph “Complex Networks: Structure and Dynamics”, published by Boccaletti in Physics Reports on 2006 converted into the most quoted paper ever appeared in the Annals of that Journal.
报告内容：
I will describe the synchronization properties of a generic networked dynamical system, and show that, under a suitable approximation, the transition to synchronization can be predicted with the only help of eigenvalues and eigenvectors of the graph Laplacian matrix. The transition comes out to be made of a well defined sequence of events, each of which corresponds to a specific clustered state. The network's nodes involved in each of the clusters can be identified, and the value of the coupling strength at which the events are taking place can be approximately ascertained. Finally, I will present large-scale simulations which show the accuracy of the approximation made, and of the predictions in describing the synchronization transition of both synthetic and real-world large size networks, and I will even report that the observed sequence of clusters is preserved in heterogeneous networks made of slightly non-identical systems.
