Predicting critical transitions with machine learning trained on surrogates of historical data
Abstract Critical transitions can occur in many natural and man-made systems. Generic early warning signals motivated by dynamical systems theory have had mixed success on real noisy data. More recent studies found that deep learning classifiers trained on synthetic data could improve performance. H...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-06-01
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| Series: | Communications Physics |
| Online Access: | https://doi.org/10.1038/s42005-025-02172-4 |
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