graph neural networks

An enhanced graph neural network with fingerprints and descriptors for molecular property prediction

Deep transfer learning with graph neural networks for the selection of manufacturing processes

A non-linear granger causality-driven dynamic graph neural network for marine buoy data imputation

Locality-Preserving Graph Laplacian Manifold Learning Based Model Predictive Control for Three-Phase Inverters

This article presents a model predictive control (MPC) strategy for three-phase inverters based on locality preserving projections (LPPs). Unlike conventional machine learning–based MPC approaches that rely on predefined or high-dimensional input features, the proposed LPP-MPC automatically extracts compact, informative representations by preserving the data’s intrinsic geometric structure. This dimensionality reduction enables fast linear control-law evaluation with computational complexity O(1), making the controller well-suited for real-time implementation. Experimental results demonstrate that the LPP-MPC achieves lower total harmonic distortion (THD) and reduced tracking error compared to quadratic-programming MPC under both linear and nonlinear load conditions, and the proposed controller maintains consistently lower THD throughout load transients than other methods such as two-degree-of-freedom MPC. Compared to existing model-free MPC and deep learning neural network, the LPP-MPC has the lowest THD and root mean square error with the least computational time owing to its efficient linear structure and strong generalization capability.

Robust and interpretable fault diagnosis for nuclear power plants using fault causal graph neural networks

Efficient real-time prediction of overpressure evolution in tunnels using graph neural networks

Identity-embedded graph neural networks for node-level cost performance index prediction in electrical construction tasks

UST-GNN: A unified spatial-topological graph neural network framework for urban analytics demonstrated through a case study on urban health prediction

Multi-scale junction temperature modeling and lifetime assessment of IGBT power modules using graph neural networks

Knowledge graph visualization and anomaly detection from cyber threat intelligence dataset using graph neural networks based methods

FedGNN-SFCP: Federated graph neural networks for ship fuel consumption prediction with heterogeneous data fusion and missing data imputation

Graph neural network-accelerated multi-objective design optimization of fractal flow fields for anion exchange membrane electrolyzers