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Deep learning for early warning signals of tipping points
Thomas M Bury1,2, R I Sujith3, Induja Pavithran4
1Department of Applied Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
Summary
A new deep learning algorithm provides early warning signals (EWS) for critical transitions in natural systems. It also predicts the nature of the new state, offering crucial insights beyond generic EWS.
Area of Science:
- Complex systems science
- Dynamical systems theory
- Machine learning applications
Background:
- Natural systems can undergo abrupt shifts called tipping points.
- Approaching tipping points often involves dynamics simplifying to predictable 'normal forms'.
- Existing early warning signals (EWS) detect transitions but not the nature of the new state.
Purpose of the Study:
- To develop a deep learning algorithm for advanced EWS.
- To enable prediction of the 'normal form' characterizing an oncoming tipping point.
- To improve sensitivity and specificity of EWS across diverse systems.
Main Methods:
- Developed a deep learning algorithm leveraging normal forms and scaling behavior near tipping points.
- Trained the algorithm on general principles, not specific systems.
- Validated the algorithm on 268 empirical and model time series from various fields.
Main Results:
- The algorithm provides highly sensitive and specific EWS in diverse systems, outperforming generic methods.
- It successfully predicts the normal form of oncoming tipping points.
- Demonstrated EWS capability in systems the algorithm was not explicitly trained on.
Conclusions:
- This deep learning approach offers a powerful tool for recognizing and predicting tipping points.
- Predicting normal forms provides qualitative insights into future system states.
- Such methods can aid in preparing for or mitigating undesirable state transitions.
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