主讲人：Reik Donner

开始时间：9月6日（周五）8:30-9:30

讲座地址：闵行校区物理楼226报告厅

报告人简介：

Since 2018 - Professor of Mathematics (DataScience and Stochastic Modeling), Magdeburg-Stendal University of AppliedSciences, Magdeburg, Germany

2014–2019 Research Group Leader, PotsdamInstitute for Climate Impact Research

2011 EGU Division Outstanding Young ScientistAward for Nonlinear Processes

2007 JSPS Postdoctoral Fellowship, 2009 GuestProfessorship, Osaka Prefecture University, Sakai, Japan

2007-2014 Postdoctoral positions at DresdenUniversity of Technology, MPI for Physics of Complex Systems (Dresden), PotsdamInstitute for Climate Impact Research, MPI for Biogeochemistry (Jena)

1997 – 2007 Study of Physics and Mathematics atPotsdam University, Germany

Division Science Officer for Time Series Analysisand Big Data of the EGU Division Nonlinear Processes

Co-PI of the Belmont Forum/JPI Climate projectGOTHAM, the German-Brazilian Research Training Group “Complex Processes inNetworks” and the EU Research Training Group CAFÉ

Editorial Board Member in currently fourinternational journals

报告内容简介:

Over the last about fourdecades, state space based methods have gained considerable importance in thefield of nonlinear time series analysis. Beyond the notion of fractaldimensions that directly derives from this framework, the concept ofrecurrences in state space and their quantitative analysis has become avaluable starting point for the detailed characterization of complex systemsbased on observational time series.

In my talk, I willdemonstrate how the proximity or similarity of dynamical states on a sampletrajectory (defining their recurrence in the state space) of a system understudy can be directly translated into a network representation in terms of arandom geometric graph, which is referred to as the associated recurrencenetwork. The potentials of this new perspective for complex system characterizationwill be discussed along with selected recent achievements in the field based onboth paradigmatic model systems and real-world observational time series.Specific emphasis will be put on the interpretation of network transitivity asa generalized fractal dimension concept, together with some theoretical andpractical challenges arising from it. Finally, I will discuss some recentachievements regarding the associated threshold selection and significancetesting problems.