As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

Read more about Artificial intelligence could change future of antimicrobial drug discovery: Here's why on Devdiscourse ...

Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...

Microgrids play a growing role in modern power systems, supporting renewable integration, local resilience, and decentralized ...

Keeping high-power particle accelerators at peak performance requires advanced and precise control systems. For example, the primary research machine at the U.S. Department of Energy's Thomas ...

Mastercard's Decision Intelligence Pro uses recurrent neural networks to analyze 160 billion yearly transactions in under 50 ...

Traditional computational electromagnetics (CEM) methods—such as MoM, FEM, or FDTD—offer high fidelity, but struggle to scale ...

Edge AI SoCs play an essential role by offering development tools that bridge the gap between AI developers and firmware ...

A new topology-based method predicts atomic charges in metal-organic frameworks from bond connectivity alone, making large-scale computational screening practical.

New research published in Scientific Reports provides evidence that the path from genetic predisposition to general ...