ArticleRoyal Society open science2022
Machine learning methods trained on simple models can predict critical transitions in complex natural systems.
Article in Royal Society open science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Tipping point forecasting in nonstationary dynamics on function spaces.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Recurrent neural chemical reaction networks trained to switch dynamical behaviours through learned bifurcations.Journal of the Royal Society, Interface · 2026Article
- Self-supervised reservoir computing with spatial-temporal encoding for identifying critical transitions.Nature communications · 2026Article
- Interpretable early warnings using machine learning in an online game-experiment.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Deep learning for disease outbreak prediction: a parallel LSTM-CNN model.Journal of the Royal Society, Interface · 2025Article
- Deep learning for predicting the occurrence of tipping points.Royal Society open science · 2025Article
- Early detection of disease outbreaks and non-outbreaks using incidence data: A framework using feature-based time series classification and machine learning.PLoS computational biology · 2025Article
- DAMP-ing IBD: Extinguish the Fire and Prevent Smoldering.Digestive diseases and sciences · 2025Review
- FateNet: an integration of dynamical systems and deep learning for cell fate prediction.Bioinformatics (Oxford, England) · 2024Article
- Early warning signals for bifurcations embedded in high dimensions.Scientific reports · 2024Article
- An early warning indicator trained on stochastic disease-spreading models with different noises.Journal of the Royal Society, Interface · 2024Article
- Optimal sampling of spatial patterns improves deep learning-based early warning signals of critical transitions.Royal Society open science · 2024Article
- Early warning signals have limited applicability to empirical lake data.Nature communications · 2023Article
- Predicting discrete-time bifurcations with deep learning.Nature communications · 2023Article
- Universal early warning signals of phase transitions in climate systems.Journal of the Royal Society, Interface · 2023Article
- Machine learning methods trained on simple models can predict critical transitions in complex natural systems.Royal Society open science · 2022Article
- Identifying critical transitions in complex diseases.Journal of biosciences · 2022Review
Corrections and comments
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Authors and funding
5 authors.
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Abstract
Forecasting sudden changes in complex systems is a critical but challenging task, with previously developed methods varying widely in their reliability. Here we develop a novel detection method, using simple theoretical models to train a deep neural network to detect critical transitions-the Early Warning Signal Network (EWSNet). We then demonstrate that this network, trained on simulated data, can reliably predict observed real-world transitions in systems ranging from rapid climatic change to the collapse of ecological populations. Importantly, our model appears to capture latent properties in time series missed by previous warning signals approaches, allowing us to not only detect if a transition is approaching, but critically whether the collapse will be catastrophic or non-catastrophic. These novel properties mean EWSNet has the potential to serve as an indicator of transitions across a broad spectrum of complex systems, without requiring information on the structure of the system being monitored. Our work highlights the practicality of deep learning for addressing further questions pertaining to ecosystem collapse and has much broader management implications.
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