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 …

Despite great progress in simulating multiphysics problems using the numerical
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …

Computational models in neuroscience usually take the form of systems of differential
equations. The behaviour of such systems is the subject of dynamical systems theory …

Reservoir computing is a best-in-class machine learning algorithm for processing
information generated by dynamical systems using observed time-series data. Importantly, it …

Neural operators map multiple functions to different functions, possibly in different spaces,
unlike standard neural networks. Hence, neural operators allow the solution of parametric …

One of the most promising applications of machine learning in computational physics is to
accelerate the solution of partial differential equations (PDEs). The key objective of machine …

Recent years have seen the rapid growth and development of the field of smart photonics,
where machine-learning algorithms are being matched to optical systems to add new …

Understanding the fundamental relationships between physics and its information-
processing capability has been an active research topic for many years. Physical reservoir …

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 present a machine learning framework bridging manifold learning, neural networks,
Gaussian processes, and Equation-Free multiscale approach, for the construction of …