Speaker
Shan Gao
(University of Alberta)
Description
Tipping points (critical transitions) are abrupt, often irreversible shifts in dynamical systems that can be triggered by small changes. This talk tackles two questions: how to predict tipping early and why tipping occurs. In this talk, I will demonstrate how combining tipping-point ideas with machine learning can enhance early-stage outbreak prediction using limited epidemic data. I will then present a new mechanism, frequency-induced tipping, where a system can be driven to a critical transition by subthreshold periodic forcing at particular frequencies, even in the absence of noise and without crossing a static bifurcation threshold.
Author
Shan Gao
(University of Alberta)
