Predicting extreme events from data using deep machine learning: When and where

Predicting extreme events from data using deep machine learning: When and where

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 tw...

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Bibliographic Details
Main Authors: Grebogi, C. (Author), Huang, Z.-G (Author), Jiang, J. (Author), Lai, Y.-C (Author)
Format: Article
Language:English
Published: American Physical Society 2022
Subjects:
Complex Ginzburg-landau equations
Convolutional neural networks
Deep neural networks
Economic and social effects
Extreme events
Forecasting
Labeling scheme
Model free
Nonlinear physical systems
Space-based
Spatial dimension
Synthetic data
Time horizons
Two-dimensional
Wind
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