On explaining the surprising success of reservoir computing forecaster of chaos? The universal machine learning dynamical system with contrast to VAR and DMD

Embedding and approximation theorems for echo state networks

(2020) Allen Hart et al.   NEURAL NETWORKS

Network structure effects in reservoir computers

(2019) T. L. Carroll et al.   CHAOS

Information dynamics with confidence: Using reservoir computing to construct confidence intervals for information-dynamic measures

(2019) David Darmon et al.   CHAOS

Chaos in time delay systems, an educational review

(2019) Hendrik Wernecke et al.   PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS

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

Observing spatio-temporal dynamics of excitable media using reservoir computing

(2018) Roland S. Zimmermann 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

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

(2018) Pantelis R. Vlachas et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

On Matching, and Even Rectifying, Dynamical Systems through Koopman Operator Eigenfunctions

(2018) Erik M. Bollt et al.   SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS

Rapid time series prediction with a hardware-based reservoir computer

(2018) Daniel Canaday et al.   CHAOS

Deep learning algorithm for data-driven simulation of noisy dynamical system

(2018) Kyongmin Yeo et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Ergodic Theory, Dynamic Mode Decomposition, and Computation of Spectral Properties of the Koopman Operator

(2017) Hassan Arbabi et al.   SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS

Using recurrent neural network models for early detection of heart failure onset

(2016) Edward Choi et al.   JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION

Recurrent neural network and a hybrid model for prediction of stock returns

(2015) Akhter Mohiuddin Rather et al.   EXPERT SYSTEMS WITH APPLICATIONS

A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition

(2015) Matthew O. Williams et al.   JOURNAL OF NONLINEAR SCIENCE

Causal Network Inference by Optimal Causation Entropy

(2015) Jie Sun et al.   SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS

A Unified Framework for Reservoir Computing and Extreme Learning Machines based on a Single Time-delayed Neuron

(2015) S. Ortín et al.   Scientific Reports

A review of unsupervised feature learning and deep learning for time-series modeling

(2014) Martin Längkvist et al.   PATTERN RECOGNITION LETTERS

Experimental demonstration of reservoir computing on a silicon photonics chip

(2014) Kristof Vandoorne et al.   Nature Communications

Applied Koopmanism

(2012) Marko Budišić et al.   CHAOS

Dynamic mode decomposition of numerical and experimental data

(2010) PETER J. SCHMID   JOURNAL OF FLUID MECHANICS

Effects of connectivity structure of complex echo state network on its prediction performance for nonlinear time series

(2010) Qingsong Song et al.   NEUROCOMPUTING

Spectral analysis of nonlinear flows

(2009) CLARENCE W. ROWLEY et al.   JOURNAL OF FLUID MECHANICS