Reservoir computing (RC), as a brain-inspired neuromorphic computing algorithm, is
capable of fast and energy-efficient temporal data analysis and prediction. Hardware …

Neuromorphic computing has been attracting ever‐increasing attention due to superior
energy efficiency, with great promise to promote the next wave of artificial general …

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

In-materia reservoir computing (RC) leverages the intrinsic physical responses of functional
materials to perform complex computational tasks. Magnetic metamaterials are exciting …

In the present paper, we propose and experimentally verify a concept of a magnonic
reservoir computer. The system utilizes the nonlinear behavior of propagating magnetostatic …

Neural networks have revolutionized the area of artificial intelligence and introduced
transformative applications to almost every scientific field and industry. However, this …

Spintronic nanodevices have ultrafast nonlinear dynamic and recurrence behaviors on a
nanosecond scale that promises to enable a high-performance spintronic reservoir …

More than 3.5 billion people live in rural areas, where water and water energy resources
play an important role in ensuring sustainable and productive rural economies. This article …

Recent studies have shown that nonlinear magnetization dynamics excited in
nanostructured ferromagnets are applicable to brain-inspired computing such as physical …

The concept of reservoir computing emerged from a specific machine learning paradigm
characterized by a three-layered architecture (input, reservoir, and output), where only the …