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

DOI10.1098/rspa.2017.0844zbMath1402.92030arXiv1802.07486OpenAlexW3105919389WikidataQ89056306 ScholiaQ89056306MaRDI QIDQ4557699

Wonmin Byeon, Zhong Yi Wan, Pantelis R. Vlachas, Themistoklis P. Sapsis, Petros Koumoutsakos

Publication date: 26 November 2018

Published in: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1802.07486

zbMATH Keywords

Gaussian processes long short-term memory T21 barotropic climate model data-driven forecasting Lorenz 96

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Neural networks for/in biological studies, artificial life and related topics (92B20) Geostatistics (86A32)

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