Topological spin textures in magnetic materials and arrangements of electric dipoles in
ferroelectrics are considered to be promising candidates for next-generation information …

Physical reservoir computing is a form of neuromorphic computing that harvests the dynamic
properties of materials for high-efficiency computing. A wide range of physical systems can …

Physical neuromorphic computing, exploiting the complex dynamics of physical systems,
has seen rapid advancements in sophistication and performance. Physical reservoir …

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

Learning and pattern recognition inevitably requires memory of previous events, a feature
that conventional CMOS hardware needs to artificially simulate. Dynamical systems …

Physical reservoir computing is a promising way to develop efficient artificial intelligence
using physical devices exhibiting nonlinear dynamics. Although magnetic materials have …

Physical reservoir computing, which is a promising method for the implementation of highly
efficient artificial intelligence devices, requires a physical system with nonlinearity, fading …

Physical neural networks are artificial neural networks that mimic synapses and neurons
using physical systems or materials. These networks harness the distinctive characteristics …

With the advent of Big Data, traditional digital computing is struggling to cope with intricate
tasks related to data classification or pattern recognition. To mitigate this limitation, software …

Physical reservoir computing (RC) is a machine learning algorithm that employs the
dynamics of a physical system to forecast highly nonlinear and chaotic phenomena. In this …