Controlling electromagnetic waves and information simultaneously by information
metasurfaces is of central importance in modern society. Intelligent metasurfaces are smart …

Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing
and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical …

Reservoir computing offers a powerful neuromorphic computing architecture for
spatiotemporal signal processing. To boost the power efficiency of the hardware …

Time series forecasting models that use the past information of exogenous or endogenous
sequences to forecast future series play an important role in the real world because most …

Constructing crossmodal in-sensor processing system based on high-performance flexible
devices is of great significance for the development of wearable human-machine interfaces …

Abstract Nanowire Networks (NWNs) belong to an emerging class of neuromorphic systems
that exploit the unique physical properties of nanostructured materials. In addition to their …

In recent years, machine-learning techniques, particularly deep learning, have outperformed
traditional time-series forecasting approaches in many contexts, including univariate and …

Reservoir computing is a concept involving mapping signals onto a high-dimensional phase
space of a dynamical system called “reservoir” for subsequent recognition by an artificial …

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 …

Ising machines, which are hardware implementations of the Ising model of coupled spins,
have been influential in the development of unsupervised learning algorithms at the origins …