Загрузка...
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...
Сохранить в:
| Опубликовано в: : | Proc Math Phys Eng Sci |
|---|---|
| Главные авторы: | , , , , |
| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
The Royal Society Publishing
2018
|
| Предметы: | |
| Online-ссылка: | 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 |
| Метки: |
Добавить метку
Нет меток, Требуется 1-ая метка записи!
|