COMPRESSIVE SENSING BASED PREDICTION OF COMPLEX DYNAMICS AND COMPLEX NETWORKS
讲座名称：
COMPRESSIVE SENSING BASED PREDICTION OF COMPLEX DYNAMICS AND COMPLEX NETWORKS
讲座时间：
2016-03-07
讲座人：
C. Grebogi
形式：
校区：
兴庆校区
实践学分：
讲座内容：
应航天航空学院强度与振动国家重点实验室邀请，英国阿伯丁大学复杂系统与数学生物研究所C. Grebogi教授将来我校进行学术交流访问并做两次学术报告. 学术报告安排如下：
讲座题目： COMPRESSIVE SENSING BASED PREDICTION OF COMPLEX DYNAMICS AND COMPLEX NETWORKS
报告时间：2016年3月7日（星期一）下午4:00--5:30
报告地点：航天学院第2会议室
讲座人：C. Grebogi教授
摘 要：
In the fields of complex dynamics and complex networks, the reverse engineering, systems identification, or 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. Given such a system, uncovering the interacting structure of the underlying network is key to understanding its collective dynamics. Based on compressive sensing, we develop an efficient approach to reconstructing complex networks under game-based interactions from small amounts of data. The method is validated by using a variety of model networks and by conducting an actual experiment to reconstruct a social network. While most existing methods in this area assume oscillator networks that generate continuous-time data, our work successfully demonstrates that the extremely challenging problem of reverse engineering of complex networks can also be addressed even when the underlying dynamical processes are governed by realistic, evolutionary-game type of interactions in discrete time. I will also touch on the issue of detecting hidden nodes, on how to ascertain its existence and its location in the network, this being highly relevant to metabolic networks.
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