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

Quantum reservoir computing (QRC) exploits the information-processing capabilities of
quantum systems to solve nontrivial temporal tasks, improving over their classical …

Using the machine learning approach known as reservoir computing, it is possible to train
one dynamical system to emulate another. We show that such trained reservoir computers …

A reservoir computer is a complex nonlinear dynamical system that has been shown to be
useful for solving certain problems, such as prediction of chaotic signals, speech …

Several recent papers have shown that reservoir computers are useful for analyzing and
predicting dynamical systems. Reservoir computers have also been shown to be useful for …

A new explanation of the geometric nature of the reservoir computing (RC) phenomenon is
presented. RC is understood in the literature as the possibility of approximating input–output …

Neural networks are transforming the field of computer algorithms, yet their emulation on
current computing substrates is highly inefficient. Reservoir computing was successfully …

We analyze the practices of reservoir computing in the framework of statistical learning
theory. In particular, we derive finite sample upper bounds for the generalization error …

In recent years, the artificial intelligence community has seen a continuous interest in
research aimed at investigating dynamical aspects of both training procedures and machine …

Numerical evaluations of the memory capacity (MC) of recurrent neural networks reported in
the literature often contradict well-established theoretical bounds. In this paper, we study the …