Frontiers

Graph Learning and Deep Unfolding for Electromagnetic Field Modelling and Antenna Systems

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

Multi-Scale Spatiotemporal Dynamic Graph Neural Network for Early Prediction of Mortality Risks in Heart Failure Patients

Abstract: Heart Failure (HF) stands as a principal public health issue worldwide, imposing a significant burden on healthcare systems. While existing prognostic methods have achieved certain ...
Dot Physics on MSN

Modeling sliding bead on tilting wire using Python | Lagrangian explained

Explore advanced physics with **“Modeling Sliding Bead On Tilting Wire Using Python | Lagrangian Explained.”** In this tutorial, we demonstrate how to simulate the motion of a bead sliding on a ...
IEEE

Transformer and Graph Neural Network-Based Federated Learning for Cellular Traffic Prediction With Sustainability Analysis

Abstract: Mobile network traffic prediction is critical for efficient network management in 5G and future 6G systems. Federated Learning (FL) enables multiple base stations (BSs) to collaboratively ...