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This study introduces a parallel machine learning approach to predict complex, chaotic systems using historical data and imperfect models. The method enhances scalability and significantly reduces the data needed for accurate spatiotemporal predictions.

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Area of Science:

  • Dynamical Systems
  • Machine Learning
  • Computational Science

Background:

  • Predicting large, spatiotemporally chaotic dynamical systems is crucial for applications like weather forecasting.
  • Existing methods often struggle with large-scale systems and rely on imperfect models and historical data.

Purpose of the Study:

  • To develop a scalable machine learning framework for predicting the time evolution of complex dynamical systems.
  • To integrate historical time series data with imperfect models for improved predictive accuracy.
  • To reduce the amount of training data required for effective model training.

Main Methods:

  • A parallel machine learning prediction scheme was developed.
  • A hybrid composite prediction system combining knowledge-based and machine learning components was employed.
  • The approach was designed for scalability to very large and complex systems.

Main Results:

  • The proposed method demonstrates excellent performance and scalability for large dynamical systems.
  • Parallelization significantly reduces the time series data needed for training machine learning components.
  • The scheme effectively incorporates subgrid-scale dynamics using training data for improved closure.

Conclusions:

  • The combined parallel and hybrid machine learning approach offers a scalable and data-efficient solution for predicting chaotic dynamical systems.
  • This method enhances the accuracy of predictions by effectively handling subgrid-scale processes.
  • The findings have significant implications for fields requiring accurate forecasting of complex systems.