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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 short-term 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 se...
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| Publicado no: | Proc Math Phys Eng Sci |
|---|---|
| Main Authors: | , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
The Royal Society Publishing
2018
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5990702/ https://ncbi.nlm.nih.gov/pubmed/29887750 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1098/rspa.2017.0844 |
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