Research
Research Themes
Complex dynamics investigates the theories and laws governing unconventional evolutionary behaviors of dynamical systems, including chaos, bifurcation, synchronization, pattern formation and collective emergence.
This theme integrates machine learning, reservoir computing, physics-guided modeling and critical-phenomena analysis to study the structure, dynamics and predictability of complex systems.
Representative researches covering ENSO prediction methods, the spring predictability barrier, and localized impacts of El Niño on global climate networks, combining complexity metrics, climate networks and physics-informed machine learning.
Long-range climate connections and interactions among Earth-system tipping elements, linking Arctic connectivity, tipping-element teleconnections, and cascading global climate risks.
Based on graph theory and statistical physics, complex network research characterizes the connection topologies of various real-world systems and investigates their structural features, dynamic evolution, propagation synchronization, robustness and control laws.