报告题目：Dimensionality reduction of nonlinear oscillators via Koopman operator theory
报告人：Prof.Hiroya Nakao（Institute of Science Tokyo）
报告时间：2024年11月13日（周三）上午10:00-11:30
报告地点： A18-526报告厅
主办单位：必威西汉姆官网平台、航空航天结构力学及控制全国重点实验室、国际合作处
报告内容摘要：
In analyzing synchronization of stable limit-cycle oscillations, phase reduction has been one of the standard methods, allowing us to describe the oscillator dynamics under weak perturbations by using the asymptotic phase of the oscillator. Recently, the method of phase reduction has been generalized to phase-amplitude reduction to characterize deviations from the limit cycle. This generalization is based on Koopman operator theory, which clarified that the notions of asymptotic phase and amplitudes are essentially related to the Koopman eigenfunctions of the oscillator. This relation also gives the possibility to extend the phase and amplitudes to noisy oscillators, and to use data-driven methods to infer the phase and amplitudes from observed time series. In this talk, we will briefly review the relationship between phase-amplitude reduction and Koopman eigenfunctions, and discuss some attempts to infer the phase and amplitude from time series using the autoencoder framework.
报告人简介：
Prof. Hiroya Nakao is a professor at Tokyo Institute of Technology (Now with the name Institute of Science Tokyo, due to the merger of two top universities in Japan). He received his PhD degree in Physics from Kyoto University in 1999 and was a postdoctoral researcher at the University of Tokyo (1999–2000) and at RIKEN (2000–2001). He worked as an instructor and then as an assistant professor at Kyoto University. From 2011 to 2019, he was an associate professor at Tokyo Institute of Technology. And since 2019, He has been a professor at the department of systems and control engineering of Tokyo Institute of Technology. He has published peer-reviewed high-impact academic papers including those in Nature Physics, Physical Review X, Physical Review Letters, etc. His main research interests are nonlinear dynamics, stochastic processes, and their applications to self-organization phenomena such as synchronization and pattern formation.
