The booming development of artificial intelligence (AI) requires faster physical processing
units as well as more efficient algorithms. Recently, reservoir computing (RC) has emerged …

Abstract Background Lithium-ion (Li-ion) batteries are one of the most attractive and
promising energy storage systems that emerge in different industrial sectors–at the top of …

Abstract Machine learning has emerged as a popular and powerful approach for solving
problems in astrophysics. We review applications of machine learning techniques for the …

As deep neural networks (DNNs) revolutionize machine learning, energy consumption and
throughput are emerging as fundamental limitations of CMOS electronics. This has …

The groundbreaking discoveries of gravitational waves from binary black-hole mergers
[1],[2],[3] and, most recently, coalescing neutron stars [4] started a new era of Multi …

The development of reusable artificial intelligence (AI) models for wider use and rigorous
validation by the community promises to unlock new opportunities in multi-messenger …

Research process automation–the reliable, efficient, and reproducible execution of linked
sets of actions on scientific instruments, computers, data stores, and other resources–has …

Analog optical and electronic hardware has emerged as a promising alternative to digital
electronics to improve the efficiency of deep neural networks (DNNs). However, previous …

One of the key challenges of real-time detection and parameter estimation of gravitational
waves from compact binary mergers is the computational cost of conventional matched …

We present a critical analysis of physics-informed neural operators (PINOs) to solve partial
differential equations (PDEs) that are ubiquitous in the study and modeling of physics …