Operating system

Physical system

Physics

Statistical physics

Mathematical analysis

Attribution

Artificial intelligence

Psychology

Attractor

Critical point (mathematics)

Chaotic

License

Transient (computer programming)

Mathematics

Computer science

Quantum mechanics

Social psychology

Physical Review Research

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.7 MoreReceived 28 May 2020Accepted 14 January 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.013090Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasBifurcationsChaosPhysical SystemsChaotic systemsTechniquesMachine learningNonlinear Dynamics

Machine learning prediction of critical transition and system collapse