标题
Next generation reservoir computing
作者
关键词
-
出版物
Nature Communications
Volume 12, Issue 1, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2021-09-21
DOI
10.1038/s41467-021-25801-2
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- On explaining the surprising success of reservoir computing forecaster of chaos? The universal machine learning dynamical system with contrast to VAR and DMD
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- Echo State Networks trained by Tikhonov least squares are L2(μ) approximators of ergodic dynamical systems
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- Using data assimilation to train a hybrid forecast system that combines machine-learning and knowledge-based components
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- Finding nonlinear system equations and complex network structures from data: A sparse optimization approach
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- How entropic regression beats the outliers problem in nonlinear system identification
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- Combining machine learning with knowledge-based modeling for scalable forecasting and subgrid-scale closure of large, complex, spatiotemporal systems
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- Gradient based hyperparameter optimization in Echo State Networks
- (2019) Luca Anthony Thiede et al. NEURAL NETWORKS
- Forecasting chaotic systems with very low connectivity reservoir computers
- (2019) Aaron Griffith et al. CHAOS
- Reservoir Computing Universality With Stochastic Inputs
- (2019) Lukas Gonon et al. IEEE Transactions on Neural Networks and Learning Systems
- Attractor reconstruction by machine learning
- (2018) Zhixin Lu et al. CHAOS
- Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach
- (2018) Jaideep Pathak et al. PHYSICAL REVIEW LETTERS
- Determination of the Edge of Criticality in Echo State Networks Through Fisher Information Maximization
- (2018) Lorenzo Livi et al. IEEE Transactions on Neural Networks and Learning Systems
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- (2016) Steven L. Brunton et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
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