graph neural networks

Modeling adolescents' perception of cycling safety: A new approach using graph neural networks and street view imagery

Graph neural network-based multi-agent reinforcement learning for active voltage control: Performance and topology robustness

A deep reinforcement learning method based on scale perception and heterogeneous graph neural network for flexible job shop scheduling

Syntactic enhancement and redundant feature elimination in text graph neural networks for propaganda detection

Cross-domain integration of transcriptomic modalities through graph neural networks

Exploring graph neural networks for flood modeling: Challenges, opportunities, and future prospects

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