Speaker Bio

Professor Celso Grebogi is one ofthe founders in the field of international chaos dynamics. He has made pioneering work in many fields such as chaos scattering, chaos control,mutation of attraction domain and complex networks. In 1990, his pioneering work on chaos control (OGY chaos control) with two other scientists was selected by the American Physical Society as the landmark work in physics in the past 50 years. In present, he is the Sixth Century Chair at King's College, University of Aberdeen, UK. He is the Founder and Director of the Institute for Complex Systems and Mathematical Biology, whose work in systems biology and complex systems became a leading in UK. He is the Co-founder of the Aberdeen-Lanzhou-Tempe Research Centre,whose work is in the new interdisciplinary field:Relativistic Quantum Chaos.His research interests are in the area of systems biology, neurodynamics, methods to control chaos,the dynamics of spatio-temporal systems,active processes in chaotic flows,relativistic quantum chaotic systems, and nanosystems - including graphene and opto-mechanical systems.

塞尔索·格雷博吉教授是国际混沌动力学领域的奠基人之一。他在混沌散射、混沌控制、吸引域突变和复杂网络等领域做出了开创性的工作。1990年，他与另外两位科学家在混沌控制（OGY混沌控制）方面的开创性工作被美国物理学会选为过去50年来物理学界的标志性工作。目前，他是英国阿伯丁大学国王学院第六世纪主席。他是复杂系统和数学生物学研究所的创始人和所长，其在系统生物学和复杂系统方面的工作成为英国的领先者。他是阿伯丁兰州Tempe研究中心的联合创始人，该中心的工作领域是新的跨学科领域：相对论量子混沌。他的研究兴趣是系统生物学、神经动力学、控制混沌的方法、时空系统的动力学、混沌流中的主动过程、相对论量子混沌系统和纳米系统——包括石墨烯和光机械系统。

Abstract

In the fields of complex dynamics and complex networks, the inverse problem is generally regarded as hard and extremely challenging mathematically as complex dynamical systems and networks consists of a large number of interacting units. However, our ideas based on compressive sensing, in combination with innovative approaches, generates a new paradigm that offers the possibility to address the fundamental inverse problem in complex dynamics and networks. In particular, in this talk, I will argue that evolutionary games model, a common type of interactions in a variety of complex, networked, natural systems and social systems, allows the uncovering of the interacting structure of the underlying network and the understanding of its collective dynamics from small amounts of data. The method is validated by conducting an actual experiment to reconstruct a social network. Based on ecological real-world networks, I will also discuss the interplay between transients and stochasticity in empirical mutualistic networks. Focusing on the tipping-point dynamics, I will discuss the phenomena of noise-induced collapse and noise-induced recovery. Two types of noise are considered, environmental (Gaussian white) noise and state-dependent demographic noise. I will also discuss control strategies that delay the extinction and advances the recovery by controlling the decay rate of pollinators in the stochastic mutualistic complex network. The phenomena of noise-induced collapse and recovery and the associated scaling laws, and the control of tipping point strategies have implications to managing high-dimensional ecological systems.

在复杂动力学和复杂网络领域，逆问题在数学上通常被认为是困难和极具挑战性的，因为复杂的动力学系统和网络由大量相互作用的单元组成。然而，我们基于压缩感知的想法与创新方法相结合，产生了一种新的范式，为解决复杂动力学和网络中的基本逆问题提供了可能性。特别是，在本次演讲中，我将提出进化博弈模型，一种在各种复杂、网络化的自然系统和社会系统中常见的交互类型，允许从少量数据中揭示底层网络的交互结构，并理解其集体动态。该方法通过进行一个重建社交网络的实际实验来验证。基于生态现实网络，我还将讨论经验互惠网络中瞬态和随机性之间的相互作用。以引爆点动力学为重点，我将讨论噪声引起的塌陷和噪声引起的恢复现象。考虑两种类型的噪声，环境（高斯白）噪声和状态相关的人口统计噪声。我还将讨论控制策略，通过控制随机互惠复杂网络中传粉者的衰减率来延迟灭绝并促进恢复。噪声引起的塌陷和恢复现象以及相关的缩放规律，以及临界点策略的控制，对管理高维生态系统具有重要意义。