Reservoir computing is a computational framework suited for temporal/sequential data
processing. It is derived from several recurrent neural network models, including echo state …

Brain–computer interfaces (BCIs) development is closely related to physics. In this paper, we
review the physical principles of BCIs, and underlying novel approaches for registration …

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

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 machine-learning approach called “reservoir computing” has been used successfully for
short-term prediction and attractor reconstruction of chaotic dynamical systems from time …

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

Deducing the state of a dynamical system as a function of time from a limited number of
concurrent system state measurements is an important problem of great practical utility. A …

Quantum reservoir computing and quantum extreme learning machines are two emerging
approaches that have demonstrated their potential both in classical and quantum machine …

We articulate the design imperatives for machine learning based digital twins for nonlinear
dynamical systems, which can be used to monitor the “health” of the system and anticipate …

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 …