Predicting observed and hidden extreme events in complex nonlinear dynamical systems with partial observations and short training time series-Reference-Cited by-同舟云学术

Predicting observed and hidden extreme events in complex nonlinear dynamical systems with partial observations and short training time series

Published:2020-03 Issue:3 Volume:30 Page:033101
ISSN:1054-1500
Container-title:Chaos: An Interdisciplinary Journal of Nonlinear Science
language:en
Short-container-title:Chaos

Author:

Chen Nan¹,Majda Andrew J.²

Affiliation:

1. Department of Mathematics, University of Wisconsin-Madison, Madison, Wisconsin 53705, USA

2. Department of Mathematics and Center for Atmosphere Ocean Science, Courant Institute of Mathematical Sciences, New York University, New York, New York 10003, USA and Center for Prototype Climate Modeling, New York University Abu Dhabi, Saadiyat Island, Abu Dhabi 129188, UAE

Funder

Office of Naval Research

Publisher

AIP Publishing

Subject

Applied Mathematics,General Physics and Astronomy,Mathematical Physics,Statistical and Nonlinear Physics

Link

http://aip.scitation.org/doi/pdf/10.1063/1.5122199

Reference127 articles.

1. Introduction to Turbulent Dynamical Systems in Complex Systems

2. Non-linear dynamics and statistical theories for basic geophysical flows

3. Nonlinear Dynamics and Chaos

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Large-scale patterns set the predictability limit of extreme events in Kolmogorov flow;Journal of Fluid Mechanics;2024-04-30

2. High-order moment closure models with random batch method for efficient computation of multiscale turbulent systems;Chaos: An Interdisciplinary Journal of Nonlinear Science;2023-10-01

3. Nonlinear modeling of sparkling drink bubbles using a physics informed long short term memory network;Chaos, Solitons & Fractals;2023-10

4. A random batch method for efficient ensemble forecasts of multiscale turbulent systems;Chaos: An Interdisciplinary Journal of Nonlinear Science;2023-02-01

5. Chaotic time series prediction using DTIGNet based on improved temporal-inception and GRU;Chaos, Solitons & Fractals;2022-06