Machine learning prediction of critical transition and system collapse

Ling-Wei Kong, Hua-Wei Fan, Celso Grebogi, Ying-Cheng Lai

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


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-learning-based 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.
Original languageEnglish
Article number013090
Number of pages14
JournalPhysical Review Research
Issue number1
Publication statusPublished - 28 Jan 2021


  • cs.LG
  • cs.AI
  • math.DS


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