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 …

Predictive modeling of various systems around the world is extremely essential from the
physics and engineering perspectives. The recognition of different systems and the capacity …

Recurrent neurons (and in particular LSTM cells) demonstrated to be efficient when used as
basic blocks to build sequence to sequence architectures, which represent the state-of-the …

In this paper, the performance of three machine-learning methods for predicting short-term
evolution and for reproducing the long-term statistics of a multiscale spatiotemporal Lorenz …

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 …

To predict a critical transition due to parameter drift without relying on a model is an
outstanding problem in nonlinear dynamics and applied fields. A closely related problem is …

Chaotic systems are notoriously challenging to predict because of their sensitivity to
perturbations and errors due to time stepping. Despite this unpredictable behavior, for many …

Nonlinear tracking control enabling a dynamical system to track a desired trajectory is
fundamental to robotics, serving a wide range of civil and defense applications. In control …

The ability of machine learning (ML) models to “extrapolate” to situations outside of the
range spanned by their training data is crucial for predicting the long-term behavior of non …

Recurrent neural networks have recently proved the state-of-the-art approach in forecasting
complex oscillatory time series on a multi-step horizon. Researchers in the field investigated …