www.8873.com:2020-07-01 作者:    编辑:康瑶    来源:理论物理交流平台


题 目:How do you know you are in a transient?
           ——Machine learning prediction of critical transition and system collapse

报告人:Ying-Cheng Lai

时 间:2020年7月9日(周四)上午9:00



报告摘要:To predict a critical transition due to parameter drift without relying on model is an outstanding problem in nonlinear dynamics and applied fields. A closely related problem is to predict whether the system is already in or if the system will be in a transient state preceding its collapse. A model free, machine learning based solution to both problems through exploiting reservoir computing to incorporate a parameter input channel will be presented. When the machine is trained in the normal functioning regime with a chaotic attractor (i.e., before the critical transition), the transition point can be predicted accurately. Remarkably, for a parameter drift through the critical point, the machine with the input parameter channel is able to predict not only that the system will be in a transient state, but also the average transient time before the final collapse.

报告人简介:YCL earned B.S, and M.S. degrees in Optical Engineering from Zhejiang University in 1982 and 1985, and MS and PhD degrees in Physics/Nonlinear Dynamics from University of Maryland, College Park in 1989 and 1992, respectively. Currently, he is the ISS Endowed Professor of Electrical Engineering and a Professor of Physics at Arizona State University. YCL received the Presidential Early Career Award for Scientists and Engineers (PECASE) from the White House in 1997. He has been a Fellow of the American Physical Society since 1999. In 2016, he was selected by the Pentagon for the Vannevar Bush Faculty Fellowship. In 2018, he was elected as a Foreign Member of National Academy of Science and Letters of Scotland. So far, he has published 490 refereed-journal papers and a comprehensive research monograph on Transient Chaos (500 pages, Springer, 2011) , and his papers have been cited over 23,000 times (Google-Scholar, H-index 73, i10 index 368). His current research interests are nonlinear dynamics, complex networks, quantum chaos, Dirac materials physics, biological physics, data analysis, signal processing, and machine learning.

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