TY - JOUR
T1 - Predicting extreme events from data using deep machine learning
T2 - When and where
AU - Jiang, Junjie
AU - Huang, Zi Gang
AU - Grebogi, Celso
AU - Lai, Ying Cheng
N1 - Funding Information: 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. Publisher Copyright: © 2022 authors. Published by the American Physical Society.
PY - 2022/6
Y1 - 2022/6
N2 - We develop a framework based on the deep convolutional neural network (DCNN) for model-free prediction of the occurrence of extreme events both in time ("when") and in space ("where") in nonlinear physical systems of spatial dimension two. The measurements or data are a set of two-dimensional snapshots or images. For a desired time horizon of prediction, a proper labeling scheme can be designated to enable successful training of the DCNN and subsequent prediction of extreme events in time. Given that an extreme event has been predicted to occur within the time horizon, a space-based labeling scheme can be applied to predict, within certain resolution, the location at which the event will occur. We use synthetic data from the two-dimensional complex Ginzburg-Landau equation and empirical wind speed data from the North Atlantic Ocean to demonstrate and validate our machine-learning-based prediction framework. The trade-offs among the prediction horizon, spatial resolution, and accuracy are illustrated, and the detrimental effect of spatially biased occurrence of extreme events on prediction accuracy is discussed. The deep learning framework is viable for predicting extreme events in the real world.
AB - We develop a framework based on the deep convolutional neural network (DCNN) for model-free prediction of the occurrence of extreme events both in time ("when") and in space ("where") in nonlinear physical systems of spatial dimension two. The measurements or data are a set of two-dimensional snapshots or images. For a desired time horizon of prediction, a proper labeling scheme can be designated to enable successful training of the DCNN and subsequent prediction of extreme events in time. Given that an extreme event has been predicted to occur within the time horizon, a space-based labeling scheme can be applied to predict, within certain resolution, the location at which the event will occur. We use synthetic data from the two-dimensional complex Ginzburg-Landau equation and empirical wind speed data from the North Atlantic Ocean to demonstrate and validate our machine-learning-based prediction framework. The trade-offs among the prediction horizon, spatial resolution, and accuracy are illustrated, and the detrimental effect of spatially biased occurrence of extreme events on prediction accuracy is discussed. The deep learning framework is viable for predicting extreme events in the real world.
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U2 - 10.1103/PhysRevResearch.4.023028
DO - 10.1103/PhysRevResearch.4.023028
M3 - Article
SN - 2643-1564
VL - 4
JO - Physical Review Research
JF - Physical Review Research
IS - 2
M1 - 023028
ER -