graph neural networks

From Bellman to Real-Time: Extensions to Complex Weather Regimes, Physics-Informed Optimization, and Full-Scale Validation

In our earlier methodology paper, we introduced a hierarchical framework combining graph compression, Diffusion Convolutional Recurrent Neural Networks (DCRNNs), and Multi-Agent Reinforcement Learning (MARL) to approximate Bellman’s optimality principle for real-time energy system control, validated using Palm Springs,...

Artificial Intelligence-Assisted Herbal Formulation Development: From Phytochemical Intelligence to Predictive Nanodelivery and Precision Phytotherapy

Herbal medicines constitute a chemically diverse source of bioactive molecules and remain important components of traditional and complementary healthcare systems. However, the development of reproducible pharmaceutical formulations from herbal materials is complicated by variability in botanical identity, geographical...

Artificial Intelligence-Assisted Herbal Formulation Development: From Phytochemical Intelligence to Predictive Nanodelivery and Precision Phytotherapy

Herbal medicines constitute a chemically diverse source of bioactive molecules and remain important components of traditional and complementary healthcare systems. However, the development of reproducible pharmaceutical formulations from herbal materials is complicated by variability in botanical identity, geographical...

PathSepsisNet: a pathway-aware temporal graph attention network integrating inflammatory crosstalk dynamics for early prediction of multi-organ dysfunction in sepsis

Leveraging large models for domain knowledge-informed architecture search of fault diagnosis

To leverage complex domain knowledge in mechanical fault diagnosis for neural architecture search (NAS) effectively, a domain knowledge-informed architecture search approach utilizing large models is proposed. First, a multi-dimensional, standardized architecture data model is constructed. This model is populated with...

Research on Intelligent Identification of Sea Surface Targets and Sea Clutter Based on Lightweight Network

Aiming at the problem of sea clutter suppression and dim target detection in sea clutter, a sea clutter identification method based on detection sliding window convolutional neural network ( DSW-CNN ) model is proposed. Firstly, the characteristics of sea clutter are analyzed to obtain the characteristics that can dist...

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...

From Lab to Clinic: Toward Deployment-Ready Graph Neural Networks for EEG-Based Neurological Diagnosis

Hzw-ZuttoZutto/Revisiting-Locally-Differentially-Private-Graph-Neural-Networks: v1.1.2

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Probabilistic protection of smart power systems against load redistribution attacks using ai-based critical measurement selection

The modernization of smart power systems through the integration of artificial intelligence (AI), Industrial Internet of Things (IIoT), cloud computing, renewable energy resources, and advanced communication infrastructures has significantly improved operational efficiency and grid intelligence. However, the increased...

A novel data-level prompt injection attack against graph prompt learning

Graph Neural Networks (GNNs) are increasingly deployed in security-critical applications, including fraud detection, recommendation integrity analysis, and scientific knowledge mining. Recently, Graph Prompt Learning (GPL) has emerged as a parameter-efficient paradigm for adapting pretrained GNNs to downstream tasks. B...

MHGNN-M-v1.0

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