ACKNOWLEDGMENTS The work at Arizona State University was supported by AFOSR under Grant No. FA9550-21-1-0438 and by ONR under Grant No. N00014-21-1-2323. The work at Xi’an Jiaotong University was supported by the National Key R&D Program of China (Grant No. 2021ZD0201300), National Natural Science Foundation of China (Grant No. 11975178), and K. C. Wong Education Foundation.Peer reviewedPublisher PD
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Long-term forecasting of extreme events such as oceanic rogue waves, heat waves, floods, earthquakes...
Spatiotemporal data have unique properties and require specific considerations. Forecasting spatiot...
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We predict the emergence of extreme events in a parametrically driven nonlinear dynamical system usi...
Understanding extreme events and their probability is key for the study of climate change impacts, r...
In recent years, the use of deep learning methods has rapidly increased in many research fields. Sim...
We propose a physics-aware machine learning method to time-accurately predict extreme events in a tu...
Understanding extreme events and their probability is key for the study of climate change impacts, r...
To predict rare extreme events using deep neural networks, one encounters the so-called small data p...
Abstract Predicting and understanding the behavior of dynamic systems have driven advancements in va...
Extreme events are defined as events that largely deviate from the nominal state of the system as ob...
Extreme precipitation can often cause serious hazards such as flooding and landslide. Both pose a th...
International audienceWe present a method based on deep learning for detecting and localizing abnorm...
Long-term forecasting of extreme events such as oceanic rogue waves, heat waves, floods, earthquakes...
Spatiotemporal data have unique properties and require specific considerations. Forecasting spatiot...