Reservoir computing systems, a class of recurrent neural networks, have recently been
exploited for model-free, data-based prediction of the state evolution of a variety of chaotic …

Recent advances have demonstrated the effectiveness of a machine-learning approach
known as “reservoir computing” for model-free prediction of chaotic systems. We find that a …

Recent interest in exploiting machine learning for model-free prediction of chaotic systems
focused on the time evolution of the dynamical variables of the system as a whole, which …

A reservoir computer (RC) is a type of recurrent neural network architecture with
demonstrated success in the prediction of spatiotemporally chaotic dynamical systems. A …

We demonstrate the effectiveness of using machine learning for model-free prediction of
spatiotemporally chaotic systems of arbitrarily large spatial extent and attractor dimension …

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the
mechanistic processes governing the dynamics to build an approximate mathematical …

We examine the efficiency of Recurrent Neural Networks in forecasting the spatiotemporal
dynamics of high dimensional and reduced order complex systems using Reservoir …

Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical
systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn …

An approach to the time-accurate prediction of chaotic solutions is by learning temporal
patterns from data. Echo State Networks (ESNs), which are a class of Reservoir Computing …

We explore the hyperparameter space of reservoir computers used for forecasting of the
chaotic Lorenz'63 attractor with Bayesian optimization. We use a new measure of reservoir …