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Related Experiment Videos

Using machine learning to predict extreme events in complex systems.

Di Qi1,2, Andrew J Majda1,2

  • 1Department of Mathematics, Courant Institute of Mathematical Sciences, New York University, New York, NY 10012; qidi@cims.nyu.edu jonjon@cims.nyu.edu.

Proceedings of the National Academy of Sciences of the United States of America
|December 25, 2019
PubMed
Summary
This summary is machine-generated.

Deep learning accurately predicts extreme events in turbulent systems. A neural network trained on limited data successfully forecasts rare, anomalous statistics in complex dynamics, showing broad applicability.

Keywords:
anomalous extreme eventsconvolutional neural networksturbulent dynamical systems

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Area of Science:

  • Complex Systems Science
  • Turbulence and Dynamical Systems
  • Machine Learning Applications

Background:

  • Extreme events and anomalous statistics are common in natural systems, posing prediction challenges.
  • Deep learning shows promise for analyzing complex dynamical systems beyond image processing.
  • Predicting rare events in turbulent systems requires advanced modeling techniques.

Purpose of the Study:

  • To investigate deep learning strategies for predicting extreme events in turbulent dynamical systems.
  • To develop and apply a novel neural network model for capturing extreme events.
  • To assess the model's performance across various statistical regimes, including skewed distributions.

Main Methods:

  • Utilized a densely connected mixed-scale network for extreme event prediction.
  • Trained the neural network on data from the truncated Korteweg-de Vries (tKdV) model, focusing on near-Gaussian regimes.
  • Employed a relative entropy loss function and empirical partition functions to measure prediction accuracy.

Main Results:

  • The neural network successfully captured extreme events within the tKdV statistical framework.
  • The model demonstrated high skill in predicting solutions across diverse statistical regimes, including highly skewed extreme events.
  • Training data was sourced from near-Gaussian regimes, yet the network generalized to predict extreme values.

Conclusions:

  • Deep learning, specifically the proposed network architecture, is highly effective for predicting extreme events in complex turbulent systems.
  • The method shows robustness even when trained on data lacking extreme values.
  • This approach holds significant potential for application to other high-dimensional and complex systems.