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 ...
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 ...
In the AI era, pure data-driven meteorological and climate models are gradually catching up with and even surpassing traditional numerical models. However, significant challenges persist in current ...
Researchers have demonstrated a new training technique that significantly improves the accuracy of graph neural networks ...
The demand for immersive, realistic graphics in mobile gaming and AR or VR is pushing the limits of mobile hardware. Achieving lifelike simulations of fluids, cloth, and other materials historically ...
Researchers have proposed a Fourier graph neural network for estimating the state of health of lithium-ion batteries while ...
Fine-grained spatial data are critical for informed decision-making in domains ranging from economic planning to ...
The multiple condition (MC)-retention model is an uncertainty-aware graph-based neural network that predicts liquid chromatography (LC) retention times across multiple column chem ...
A universal potential for all-purpose atomic simulations has been pursued for decades, but remains challenging due to limitations in model expressiveness and dataset construction. Now, writing in the ...
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