ArticleProceedings of the National Academy of Sciences of the United States of America2021
Deep learning for early warning signals of tipping points.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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Who cites it
58 citing papers in PubMed.
- Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation.Biologia futura · 2026Review
- The Soil-Plant Coupling Principle (SPCP): A Conceptual Framework for Diagnosing Hidden Ecosystem Vulnerability.Plants (Basel, Switzerland) · 2026Article
- Adversarial dynamical systems characterize when data-driven learning succeeds or fails.Nature communications · 2026Article
- Early Warning Signals in Ecological Time-Series.Entropy (Basel, Switzerland) · 2026Review
- Self-supervised reservoir computing with spatial-temporal encoding for identifying critical transitions.Nature communications · 2026Article
- Integrating Divergence-Based Proteomic Analysis and Directed Network Diffusion to Characterize Diagnosis-Anchored Molecular Variability at the Metabolic Syndrome-Migraine Interface.International journal of molecular sciences · 2026Article
- Beyond Feature Selection: Interpretable Machine Learning for Mechanistic Insights in Metabolomics.Biology · 2026Review
- Detecting and forecasting tipping points from sample variance alone.PNAS nexus · 2026Article
- Volcanism-induced collapse and recovery of the Atlantic meridional overturning circulation under glacial conditions.Science advances · 2026Article
- Predicting instabilities in transient landforms and interconnected ecosystems.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
- A method to identify positive tipping points to accelerate low-carbon transitions and actions to trigger them.Sustainability science · 2026Article
- Article
- FatePredictor: Cell fate decision-making prediction with an ensemble deep learning model.Innovation (Cambridge (Mass.)) · 2025Article
- Anticipating depression trajectories by measuring plasticity and change through symptom network dynamics.European psychiatry : the journal of the Association of European Psychiatrists · 2025Article
- 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
- Revealing unseen dynamical regimes of ecosystems from population time-series data.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Applicability of spatial early warning signals to complex network dynamics.Journal of the Royal Society, Interface · 2025Article
- Do early warning signals of tipping points lead to better decisions?Journal of the Royal Society, Interface · 2025Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Many natural systems exhibit tipping points where slowly changing environmental conditions spark a sudden shift to a new and sometimes very different state. As the tipping point is approached, the dynamics of complex and varied systems simplify down to a limited number of possible "normal forms" that determine qualitative aspects of the new state that lies beyond the tipping point, such as whether it will oscillate or be stable. In several of those forms, indicators like increasing lag-1 autocorrelation and variance provide generic early warning signals (EWS) of the tipping point by detecting how dynamics slow down near the transition. But they do not predict the nature of the new state. Here we develop a deep learning algorithm that provides EWS in systems it was not explicitly trained on, by exploiting information about normal forms and scaling behavior of dynamics near tipping points that are common to many dynamical systems. The algorithm provides EWS in 268 empirical and model time series from ecology, thermoacoustics, climatology, and epidemiology with much greater sensitivity and specificity than generic EWS. It can also predict the normal form that characterizes the oncoming tipping point, thus providing qualitative information on certain aspects of the new state. Such approaches can help humans better prepare for, or avoid, undesirable state transitions. The algorithm also illustrates how a universe of possible models can be mined to recognize naturally occurring tipping points.
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