graph neural networks

Grow with the Flow: Scalable Graph Neural Networks for Energy Grid Reliability

Application of Graph Neural Networks in Computational Fluid Mechanics

Computational fluid dynamics (CFD) discretizes the Navier-Stokes equations on computational meshes to obtain flow-field quantities like velocity, pressure, and density through numerical iteration, but traditional solvers, though reliable in accuracy, are computationally expensive and time-consuming for high-Reynolds-nu...

Integrated GNSS/LEO precise point positioning using factor graph optimization with heterogeneous graph-based stochastic modeling

Abstract Low Earth Orbit (LEO) satellite augmentation can improve the availability and satellite geometry of Precise Point Positioning (PPP). However, most existing Global Navigation Satellite System (GNSS)/LEO PPP methods rely on fixed stochastic models and do not fully exploit the heterogeneous characteristics of GNS...

Graph Self-Supervised Learning: A Hybrid Approach Combining Contrastive and Generative Paradigms

Graph-structured data is ubiquitous, yet labeled graph data remains scarce and expensive, limiting the effectiveness of supervised graph neural networks (GNNs). To address this, self-supervised learning (SSL) has emerged as a promising paradigm to pre-train GNNs on unlabeled graphs. However, existing SSL methods typica...

Distance-Based Representation Learning with Nonlinear Feature Algebras

<jats:p> Graph neural networks and spectral embeddings aggregate local neighbourhoods and so miss the global metric properties—growth rate, hyperbolicity, boundary at infinity—that govern large-scale structure in hierarchical, networked, and negatively curved data. We propose a fr...

GCN-based learner movement feature network modeling for personalized training path planning and dynamic evaluation

Abstract Personalized training path planning has emerged as an active concern in intelligent sports education, yet many existing approaches struggle to capture the dependencies that link diverse movement features. This study develops a graph convolutional neural network (GCN) approach that models learner movement featu...

Exploring Engineers' Perspectives on the Adoption of Artificial Intelligence in Logic Circuit Design and Optimization

Abstract The increasing complexity of digital circuits has encouraged the use of Artificial Intelligence (AI) in Electronic Design Automation (EDA) to assist with logic synthesis, circuit optimization, and design analysis. Machine learning approaches, including Graph Neural Networks (GNNs) and Reinforcement Learning (R...

GOLDWALK A Marked-Walk Engine for Cyclic Groups

GOLDWALK A Marked-Walk Engine for Cyclic Groups Author: C. J. Tully ORCID: 0009-0007-5661-7332 https://doi.org/10.5281/zenodo.23021217 Version: 0.1 Date: 29 September 2026 License: CC BY 4.0 Resource type: Technical note / Software documentation --- Abstract GOLDWALK is a software and theorem-card package for enumerati...

Temporal Inflow Dynamic Estimation Network (TideNet): A novel physics-informed graph neural network for daily reservoir inflow forecasting

Graph Neural Networks in cancer genomics: Mapping mathematical operations to biological insights

Deterministic and probabilistic neural surrogates of global hybrid-Vlasov simulations

Abstract Hybrid-Vlasov simulations resolve ion-kinetic effects in the solar wind–magnetosphere interaction, but even 5D (2D + 3V) configurations are computationally expensive. We show that graph-based machine learning emulators can learn the spatiotemporal evolution of electromagnetic fields and lower-order moments of...

Derivative-Informed Graph Convolutional Autoencoder with Phase Classification for the Lifshitz-Petrich Model

The Lifshitz-Petrich (LP) model is a classical model for describing complex spatial patterns such as quasicrystals and multiphase structures. Solving and classifying the solutions of the LP model is challenging due to the presence of high-order gradient terms and the long-range orientational order characteristic of the...