graph neural networks

OISES-Graph-Surrogate: geometry-aware surrogate modelling and inverse design of origami-inspired super-expandable scaffolds

Virtual laboratory and graph neural surrogate for origami-inspired super-expandable scaffolds in distraction osteogenesis: co-rotational beam and pore-scale Stokes solvers, an STL-to-graph front end, a multi-task heteroscedastic Scaffold Graph Network, parametric baselines, and the optimisation, ablation and repetition...

OISES-Graph-Surrogate: geometry-aware surrogate modelling and inverse design of origami-inspired super-expandable scaffolds

Virtual laboratory and graph neural surrogate for origami-inspired super-expandable scaffolds in distraction osteogenesis: co-rotational beam and pore-scale Stokes solvers, an STL-to-graph front end, a multi-task heteroscedastic Scaffold Graph Network, parametric baselines, and the optimisation, ablation and repetition...

MHGNN-M-v1.0

A multi-source heterogeneous graph neural network for traffic congestion prediction with missing data

Partial differential equations in the age of machine learning: a critical synthesis of classical, machine learning, and hybrid methods

Abstract Partial differential equations (PDEs) govern physical phenomena across the full range of scientific scales, yet their computational solution remains one of the defining challenges of modern science. This critical review examines two mature but epistemologically distinct paradigms for PDE solution, classical nu...

A battery life prediction method based on graph neural network assisted by deep reinforcement learning

With the wide utilization of new energy batteries, the accurate prediction of battery life has become an urgent technical issue that needs to be addressed. Studies have developed battery life prediction models based on correlation analysis for feature extraction. This study introduces a DQN-based graph structure genera...

Improvement of personalized recommendation service level of intelligent libraries based on artificial intelligence and big data mining technology

The research conclusion confirms that the proposed model provides an advanced technical solution with both theoretical innovation and practical value for constructing an efficient and accurate personalized recommendation system for intelligent libraries of colleges and universities.

Enhancing Graph Neural Network Explainers Using a Distribution Shift Consistency-Guided Generator

Graph Neural Network (GNN) explainers aim to identify explanatory subgraphs that provide rationales for GNNs’ predictions. Nevertheless, the distribution shift of subgraphs introduces out-of-distribution (OOD) issues into GNNs’ predictions. The OOD issues compromise explainers’ optimization, as the optimization relies...

Biomimetic Spiking Neural Graph Dynamics: Neuromorphic Memory Consolidation and Neurotransmitter Modulation in Autonomous Cognitive Operating Systems

Modern autonomous agents predominantly manage state and memory through static vector embeddings and flat nearest-neighbor (k-NN) retrieval mechanisms. While effective for isolated semantic lookups, this mechanical paradigm suffers from severe cognitive pathologies in long-horizon reasoning tasks: lack of temporal recen...

Probing an industrial domain knowledge-enhanced transfer graph neural network-driven fault diagnosis approach for machine tools

Edge-to-Cloud Computations-as-a-Service in Software-Defined Energy Networks for Smart Grids

Modern electricity grids run latency-sensitive functions, protection relays, fault isolation, and microgrid control, that demand millisecond analytics at the edge, while energy-hungry analytics workloads sit in distant clouds, causing missed realtime deadlines and wasted power. We formulate the placement of these grid...

Machine Learning-Aided Voltage Stability-Constrained Optimal Power Flow Dataset and Source Code

This repository contains the dataset and source code developed for machine learning-aided voltage stability-constrained optimal power flow (ML-VSCOPF) studies on a modified IEEE 118-bus power system. The dataset contains operating conditions, network topology information, renewable-generation dispatch, load conditions,...

REAPS: An All-Atom Receptor-Aware Geometric Deep Learning Framework for De Novo Design of Linear and Macrocyclic Peptide Binders

The REceptor-Aware Peptide Sequence Designer (REAPS), a geometric graph neural network that reframes sequence design for peptide binders by treating the receptor as a fully observable all-atom context, is introduced.