2024
DOI: 10.1038/s42256-024-00937-0
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Deep learning for predicting rate-induced tipping

Yu Huang,
Sebastian Bathiany,
Peter Ashwin
et al.

Abstract: Nonlinear dynamical systems exposed to changing forcing values can exhibit catastrophic transitions between distinct states. The phenomenon of critical slowing down can help anticipate such transitions if caused by a bifurcation and if the change in forcing is slow compared with the system’s internal timescale. However, in many real-world situations, these assumptions are not met and transitions can be triggered because the forcing exceeds a critical rate. For instance, the rapid pace of anthropogenic climate … Show more

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