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

Forecasting the behavior of high-dimensional dynamical systems using machine learning
requires efficient methods to learn the underlying physical model. We demonstrate …

Forecasting is an extensive field of study, which tries to avoid injuries, diseases, and
damages but also can help in energy production, finance investments, etc. Two mathematics …

Reservoir computing (RC) is a simple and efficient model-free framework for forecasting the
behavior of nonlinear dynamical systems from data. Here, we show that there exist …

Reservoir computing is a machine learning approach that can generate a surrogate model
of a dynamical system. It can learn the underlying dynamical system using fewer trainable …

The paradigm of reservoir computing exploits the nonlinear dynamics of a physical reservoir
to perform complex time-series processing tasks such as speech recognition and …

In this work, we combine nonlinear system control techniques with next-generation reservoir
computing, a best-in-class machine learning approach for predicting the behavior of …

The rapidity and low power consumption of superconducting electronics makes them an
ideal substrate for physical reservoir computing, which commandeers the computational …

Understanding the learning dynamics and inductive bias of neural networks (NNs) is
hindered by the opacity of the relationship between NN parameters and the function …

We design scalable neural networks adapted to translational symmetries in dynamical
systems, capable of inferring untrained high-dimensional dynamics for different system …