Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks
Author(s)Vlachas, Pantelis R.; Byeon, Wonmin; Koumoutsakos, Petros; Wan, Zhong Yi; Sapsis, Themistoklis P.
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We introduce a data-driven forecasting method for high-dimensional chaotic systems using long shortterm memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high-dimensional dynamical systems in their reduced order space and are shown to be an effective set of nonlinear approximators of their attractor. We demonstrate the forecasting performance of the LSTM and compare it with Gaussian processes (GPS) in time series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation and a prototype climate model. The LSTM networks outperform the GPS in short-Term forecasting accuracy in all applications considered. A hybrid architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is proposed to ensure convergence to the invariant measure. This novel hybrid method is fully data-driven and extends the forecasting capabilities of LSTM networks.
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science
The Royal Society
Vlachas, Pantelis R., Wonmin Byeon, Zhong Y. Wan, Themistoklis P. Sapsis, and Petros Koumoutsakos. “Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks.” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science 474, no. 2213 (May 2018): 20170844. © 2018 The Authors
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