Abstract: In this letter, a novel prior physics-driven graph neural network (PPDGNN) is proposed firstly to rapidly compute the far-field scattering in the composite large-scale finite periodic ...

FastGraph is designed primarily read-heavy work. When there is a design choice between optimizing read paths vs write paths in this library, generally read paths are optimized at the expense of write ...

In this letter, a novel prior physics-driven graph neural network (PPDGNN) is proposed firstly to rapidly compute the far-field scattering in the composite large-scale finite periodic structures ...

This article introduces a model-based design, implementation, deployment, and execution methodology, with tools supporting the systematic composition of algorithms from generic and domain-specific ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Graph Neural Networks (GNNs) have emerged as powerful tools for predicting material ...

Add a description, image, and links to the graph-structures topic page so that developers can more easily learn about it.

Large Language Models (LLMs) like GPT-4, Qwen2, and LLaMA have revolutionized artificial intelligence, particularly in natural language processing. These Transformer-based models, trained on vast ...

Addressing the threats of climate change, pollution, and overfishing to marine ecosystems necessitates a deeper understanding of coastal and oceanic fluid dynamics. Within this context, Lagrangian ...

In graph analysis, the need for labeled data presents a significant hurdle for traditional supervised learning methods, particularly within academic, social, and biological networks. To overcome this ...

A new theory allows researchers to create easy-to-solve mathematical models using cables, a previously challenging mathematical problem.