Machine learning prediction of critical transition and system collapse
Ling-Wei Kong, Huawei Fan, Celso Grebogi, Ying‐Cheng Lai · Physical Review Research · 2021
To predict a critical transition due to parameter drift without relying on a model is an outstanding problem in nonlinear dynamics and applied fields.A closely related problem is to predict whether the system is already in or if the system will be in a transient state preceding its collapse.We develop a model-free, machine-learningbased solution to both problems by exploiting reservoir computing to incorporate a parameter input channel.We demonstrate that, when the machine is trained in the normal functioning regime with a chaotic attractor (i.e., before the critical transition), the transition point can be predicted accurately.Remarkably, for a parameter drift through the critical point, the machine with the input parameter channel is able to predict not only that the system will be in a transient state, but also the distribution of the transient lifetimes and their average before the final collapse, revealing an important physical property of transient chaos.