RESEARCH
Scheduling
Under construction...
Complex networks
A network is a set of items, which we will call vertices or sometimes nodes, with connections between them. The study of networks, especially collective behavior of a network, has become increasingly important in many disciplines including physics, biology and social science. When two nodes interact, they are deemed to be connected or linked to one another. The knowledge of how the nodes of a network interact or connect with one another is thus crucial for understanding the behavior and functionality of a network.
The goal of my project is to establish methods to infer the connectivity and structure of a network from measurements of the dynamics of the nodes. Recently, researchers have developed a method that can extract the connectivity of bidirectional networks with uniform coupling using measurements of the dynamics of the nodes as the only input data, which gives remarkably good accuracy for a variety of networks with different dynamics. The method is applicable to networks with uniform, bidirectional linear-diffusive coupling. However, in realistic situations, coupling strength and noise amplitude can be different for different nodes and the coupling function can be nonlinear, making the extraction of connectivity even more challenging. It is hard but feasible to broaden the method to networks with non-uniform coupling strength. I have a wealth of research experience on nonlinear science and have a strong programming skills, so the main part of my research is to make a modification of the method for more realistic networks and to test them by carrying out systematic numerical studies.
Under construction...
Complex networks
A network is a set of items, which we will call vertices or sometimes nodes, with connections between them. The study of networks, especially collective behavior of a network, has become increasingly important in many disciplines including physics, biology and social science. When two nodes interact, they are deemed to be connected or linked to one another. The knowledge of how the nodes of a network interact or connect with one another is thus crucial for understanding the behavior and functionality of a network.
The goal of my project is to establish methods to infer the connectivity and structure of a network from measurements of the dynamics of the nodes. Recently, researchers have developed a method that can extract the connectivity of bidirectional networks with uniform coupling using measurements of the dynamics of the nodes as the only input data, which gives remarkably good accuracy for a variety of networks with different dynamics. The method is applicable to networks with uniform, bidirectional linear-diffusive coupling. However, in realistic situations, coupling strength and noise amplitude can be different for different nodes and the coupling function can be nonlinear, making the extraction of connectivity even more challenging. It is hard but feasible to broaden the method to networks with non-uniform coupling strength. I have a wealth of research experience on nonlinear science and have a strong programming skills, so the main part of my research is to make a modification of the method for more realistic networks and to test them by carrying out systematic numerical studies.