graph neural networks

Graph-structured hyperdimensional computing for data-efficient and explainable process–structure–property prediction

Deep learning of proteomics data

Next-generation sequencing technology has propelled the field of biology into the big data era, and continual advancements in computing have now made it easier to explore complex biological systems. However, analysing such highly complex data with conventional machine learning algorithms can be troublesome as these tec...

Deep learning for boundary representation CAD models

This thesis explores utilising deep learning methodologies for tasks relating to learning from boundary representation (B-Rep) CAD models. The ambition is to use deep learning for an automatic feature recognition algorithm to identify geometric features to help automate the CAD to analysis pre-processing task of defeat...

Is the graph doing anything? A sourced survey of recurrent self-routing neural graphs as an alternative to layered networks

Sourced survey with a public quote-per-claim evidence ledger on whether a persistent, self-routing recurrent graph of neural nodes gives more reasoning capacity per stored parameter than a weight-shared looped Transformer, plus a preregistered matched experimental protocol (not run). Built with AI agents; see README.

Is the graph doing anything? A sourced survey of recurrent self-routing neural graphs as an alternative to layered networks

Sourced survey with a public quote-per-claim evidence ledger on whether a persistent, self-routing recurrent graph of neural nodes gives more reasoning capacity per stored parameter than a weight-shared looped Transformer, plus a preregistered matched experimental protocol (not run). Built with AI agents; see README.

PIGNN3D: an accelerated physics-informed graph neural network for 3D thermal field simulation in data centers

Efficient thermal management is critical in data centers, where computational fluid dynamics (CFD) simulations provide high-fidelity airflow and temperature predictions but remain computationally demanding and time-intensive. While data-driven methods have emerged as promising alternatives, most existing works are limi...

AI-Driven Data Governance Framework for Enterprises in Cloud Environments: Design, Implementation, and Enterprise Evaluation

Challenges cloud-based enterprise data governance is encountering include data growth, regulatory changes, and the inflexibility of traditional rule-based systems. This systematic literature review, based on PRISMA guidelines, analyses 67 peer-reviewed papers (2019–2026) under three research questions related to AI-dri...

Data-augmented crystal graph convolutional neural network for uncertainty-aware thermodynamic stability prediction of two-dimensional MXene/MoS2 materials

SupCL-GNN: Supervised contrastive learning with graph neural networks for enhanced encrypted traffic classification

An Optimization Framework for Intraoperative Suture Planning via Physics-Aware Differentiable Graph Neural Network-Based Surrogates

Physics-Informed Surrogate Statistical Static Timing Analysis: An Honest Account of Feature Injection, Architectural Bias, and Conformal Calibration for Graph Neural Network Surrogates

Cybersecurity risk assessment and defense strategies for AI-based power trading systems

To ensure the safe, stable, and reliable operation of the artificial intelligence power trading system, and to overcome the shortcomings of existing risk assessment methods such as relying on labeled data, difficulty tracking risk causality, and poor real-time performance, this study proposes an evaluation method that...