A new research model called PiGRAND merges physics guidance with graph neural diffusion to predict and control AM processes.

Graph neural networks (GNNs) have rapidly emerged as a central methodology for analysing complex datasets presented as graphs, where entities are interconnected through diverse relationships. By ...

Urban congestion is a big problem in our cities. It leads to commuter delays and economic inefficiency. More tragically, ...

Graph Neural Networks (GNNs) and GraphRAG don’t “reason”—they navigate complex, open-world financial graphs with traceable, multi-hop evidence. Here’s why BFSI leaders should embrace graph-native AI ...