ArticleProceedings of the National Academy of Sciences of the United States of America2020
Using machine learning to predict extreme events in complex systems.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
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- Interpretable flash flood susceptibility mapping in Yarlung Tsangpo River Basin using H2O Auto-ML.Scientific reports · 2025Article
- Utilizing non-stationary extreme value model to quantify extreme rainfall in two major cities in Bangladesh.Stochastic environmental research and risk assessment : research journal · 2025Article
- Cooperative control of environmental extremes by artificial intelligent agents.Journal of the Royal Society, Interface · 2024Article
- Unambiguous Models and Machine Learning Strategies for Anomalous Extreme Events in Turbulent Dynamical System.Entropy (Basel, Switzerland) · 2024Article
- Developing Explainable Deep Model for Discovering Novel Control Mechanism of Neuro-Dynamics.IEEE transactions on medical imaging · 2024Article
- Review
- Evaluation of Model-Based PMEnvironmental science & technology · 2023Article
- Data-driven prediction in dynamical systems: recent developments.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Digital rheometer twins: Learning the hidden rheology of complex fluids through rheology-informed graph neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Model-free detection of unique events in time series.Scientific reports · 2022Article
- Collective correlations, dynamics, and behavioural inconsistencies of the cryptocurrency market over time.Nonlinear dynamics · 2022Article
- Entanglement-Structured LSTM Boosts Chaotic Time Series Forecasting.Entropy (Basel, Switzerland) · 2021Article
- An adaptive approach to machine learning for compact particle accelerators.Scientific reports · 2021Article
- Machine learning dismantling and early-warning signals of disintegration in complex systems.Nature communications · 2021Article
- Article
- A theoretical model of health management using data-driven decision-making: the future of precision medicine and health.Journal of translational medicine · 2021Article
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2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Extreme events and the related anomalous statistics are ubiquitously observed in many natural systems, and the development of efficient methods to understand and accurately predict such representative features remains a grand challenge. Here, we investigate the skill of deep learning strategies in the prediction of extreme events in complex turbulent dynamical systems. Deep neural networks have been successfully applied to many imaging processing problems involving big data, and have recently shown potential for the study of dynamical systems. We propose to use a densely connected mixed-scale network model to capture the extreme events appearing in a truncated Korteweg-de Vries (tKdV) statistical framework, which creates anomalous skewed distributions consistent with recent laboratory experiments for shallow water waves across an abrupt depth change, where a remarkable statistical phase transition is generated by varying the inverse temperature parameter in the corresponding Gibbs invariant measures. The neural network is trained using data without knowing the explicit model dynamics, and the training data are only drawn from the near-Gaussian regime of the tKdV model solutions without the occurrence of large extreme values. A relative entropy loss function, together with empirical partition functions, is proposed for measuring the accuracy of the network output where the dominant structures in the turbulent field are emphasized. The optimized network is shown to gain uniformly high skill in accurately predicting the solutions in a wide variety of statistical regimes, including highly skewed extreme events. The technique is promising to be further applied to other complicated high-dimensional systems.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.