Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks

Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks

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...

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Bibliographic Details
Main Authors: Vlachas, Pantelis R., Byeon, Wonmin, Koumoutsakos, Petros, Wan, Zhong Yi, Sapsis, Themistoklis P.
Other Authors: Massachusetts Institute of Technology. Department of Mechanical Engineering
Format: Article
Published: The Royal Society 2019
Online Access:http://hdl.handle.net/1721.1/120011
https://orcid.org/0000-0001-7264-3628
https://orcid.org/0000-0003-0302-0691

Internet

http://hdl.handle.net/1721.1/120011
https://orcid.org/0000-0001-7264-3628
https://orcid.org/0000-0003-0302-0691

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