Previous Talks

Prof. Edward Ott, U. Maryland

Prediction of Chaotic Dynamical Systems using Machine Learning

November 29, 2021 – 10:00 a.m. (Eastern Standard Time)

Prof. Edward Ott will discuss the use of machine learning for predicting the future evolution of dynamical systems, including systems that are very large, complex, and chaotic. He will explain reservoir computing, the basic machine learning method used in this talk. Following that, he will illustrate prediction on simple systems, hybrid prediction combining machine learning with physical knowledge, and a parallel configuration for treating large spatiotemporally chaotic systems. Illustrations and recent progress on applications to terrestrial weather and climate prediction will be presented.

Professor Ott’s current research is on the basic theory and applications of nonlinear dynamics. Some of his current research projects are in wave chaos, dynamics on large interconnected networks, chaotic dynamics of fluids, and weather prediction. Professor Ott is a fellow of the American Physical Society, the Institute of Electrical and Electronics Engineers, and the Society for Industrial and Applied Mathematics (SIAM). He is the recipient of the APS Julius Edgar Lilienfield Prize for 2014.

Photo credits: https://www.ae-info.org/ae/Member/Ott_Edward

Prof. Kazuyuki Aihara, U. Tokyo

Data Analysis on Critical Transitions in Complex Systems and its Application to Early Precision Medicine

October 25, 2021 – 09:00 pm (Japan Standard Time)

Prof. Kazuyuki Aihara reviewed his group’s recent studies on DNB (Dynamical Network Biomarkers) that provide early warning signals of imminent bifurcation from a healthy state to a disease state through a pre-disease state. He also explained the possible application of DNB for early precision medicine.

Kazuyuki Aihara received a B.E. degree in electrical engineering and Ph.D. degree in electronic engineering from the University of Tokyo (UTokyo), Tokyo, Japan, in 1977 and 1982, respectively. He led the ERATO (Exploratory Research for Advanced Technology) Aihara Complexity Modelling project by JST (Japan Science and Technology Agency) from 2003 to 2008 and the FIRST Innovative Mathematical Modelling project by JSPS (Japan Society for the Promotion of Science) through the FIRST (Funding Program for World-Leading Innovative R&D Science and Technology) program from 2010 to 2014 designed by CSTP (Council for Science and Technology Policy). Currently, he is University Professor and Professor Emeritus of UTokyo,  Deputy Director at the International Research Center for Neurointelligence (IRCN) at UTokyo, and Project Manager of the Moonshot project by the cabinet office of the Japanese government on “Comprehensive Mathematical Understanding of the Complex Control System between Organs and Challenge for Ultra-Early Precision Medicine.”

Photo credits: https://ircn.jp/en/mission/people/kazuyuki_aihara

Prof. Jürgen Kurths, PIK Germany

Exploring Predictability of Extreme Climate Events via a Complex Network Approach

September 27, 2021 – 04:00 pm (Central European Time)

Earth is a complex system whose dynamics involve innumerous interactions and multiple feedbacks. This makes predictions and risk analysis of very strong, and sometime extreme events such as floods, landslides, heatwaves, earthquakes etc., a challenging task. Here, I will introduce a recently developed approach via complex networks to analyze strong climate events. This leads to an inverse problem: Is there a backbone-like structure underlying the climate system? Towards this, we propose a method to reconstruct and analyze a complex network from observational and reanalysis data. This approach enables us to uncover relations to global and regional circulation patterns in oceans and atmosphere, which leads to substantially better predictions of high-impact phenomena, in particular of the Indian Summer Monsoon, El Nino events, droughts in the central Amazon, extreme rainfall in the Eastern Central Andes, and the Pacific decadal oscillation. I argue that network-based approaches can significantly complement numerical modeling for better predictions of the extreme weather events and lead to a better understanding of meteorological data.

Prof. Jürgen Kurths is Senior Advisor at Research Department for “Complexity Science” at Potsdam Institute for Climate Impact Research (PIK) as well as Professor and Senior Advisor at Humboldt University Berlin.

Photo credits: https://www.pik-potsdam.de/members/kurths/homepage