Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model

@article{Pathak2018HybridFO,
  title={Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model},
  author={Jaideep Pathak and A. Wikner and R. Fussell and Sarthak Chandra and B. Hunt and M. Girvan and E. Ott},
  journal={Chaos},
  year={2018},
  volume={28 4},
  pages={
          041101
        }
}
  • Jaideep Pathak, A. Wikner, +4 authors E. Ott
  • Published 2018
  • Computer Science, Physics, Mathematics, Medicine
  • Chaos
  • A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the mechanistic processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine… CONTINUE READING
    106 Citations
    Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems
    • 9
    • PDF
    Applying Machine Learning to Improve Simulations of a Chaotic Dynamical System Using Empirical Error Correction
    • P. Watson
    • Computer Science, Medicine
    • Journal of advances in modeling earth systems
    • 2019
    • 15
    • PDF
    Learning Ergodic Averages in Chaotic Systems
    • 2
    • Highly Influenced
    • PDF
    Using machine learning to correct model error in data assimilation and forecast applications
    • 1
    • PDF
    Attractor reconstruction by machine learning.
    • 110
    • PDF

    References

    SHOWING 1-10 OF 18 REFERENCES
    A hybrid neural network‐first principles approach to process modeling
    • 542
    • PDF
    Reservoir computing approaches to recurrent neural network training
    • 1,393
    • PDF
    Reservoir computing with a single time-delay autonomous Boolean node
    • 57
    • PDF
    Long Short-Term Memory
    • 35,872
    • PDF