graph neural networks

Recent Advances In Bearing Remaining Useful Life Prediction: A Review Of Machine Learning And Deep Learning Approaches

Rolling element bearings are critical components of rotating machinery and are highly prone to degradation and failure during prolonged operation, making accurate Remaining Useful Life (RUL) prediction essential for predictive maintenance, reducing unexpected downtime, and improving system reliability and safety. In re...

Dataset and Implementation Codes for Resilient Real-Time Metro Rerouting Using Adaptive Bidirectional Heuristics and Graph Neural Networks

This repository contains the dataset, source codes, trained graph models, and experimental outputs used in the research article entitled: "Resilient Real-Time Metro Rerouting with Adaptive Bidirectional Heuristics and Graph Neural Network-Based Edge Prediction" The provided materials support the implementation and eval...

Physics-Informed Neural Networks as Differentiable Surrogates for 4D-Variational Data Assimilation

Biogeochemical forecasting requires state estimation methods that accommodate sparse observations, nonlinear dynamics, and discontinuous ecological processes. Four-dimensional variational data assimilation (4D-Var) addresses this challenge but depends on tangentlinear and adjoint models that are costly to derive and ma...

Physics-Informed Digital Twins for Real-Time Health Monitoring Performance Prediction and Remaining Useful Life Assessment of Gas Turbines Foundations and Structural Support Systems

Gas turbines and their foundations and structural support systems operate as dynamically coupled thermo-mechanical assets whose degradation is governed by interacting aerodynamic, thermal, rotational, vibration, fatigue, settlement, stiffness-loss, and load-transfer mechanisms. Conventional condition-monitoring and rem...

Recent Advances In Bearing Remaining Useful Life Prediction: A Review Of Machine Learning And Deep Learning Approaches

Rolling element bearings are critical components of rotating machinery and are highly prone to degradation and failure during prolonged operation, making accurate Remaining Useful Life (RUL) prediction essential for predictive maintenance, reducing unexpected downtime, and improving system reliability and safety. In re...

Heterogeneous fairness-aware adaptive feature enhancement framework for graph neural networks learning

Current Graph Neural Networks (GNNs), which predominantly rely on the homophily assumption, often amplify biases and degrade performance for minority groups in real-world graphs characterized by prevalent heterophilic structures. Current methods face limitations due to either dependence on sensitive attributes or separ...

Interpretable Deep Learning Reveals Hydro-Geomorphic Controls on Flood Susceptibility and Exposure in Data-Scarce Regions

Description This repository contains the source code, model architectures, and analysis workflows used in the study: “Interpretable Deep Learning Reveals Hydro-Geomorphic Controls on Flood Susceptibility and Exposure in Data-Scarce Regions” The study develops a geomorphology-driven flood susceptibility (FS) framework u...

Digital Twin-Assisted Beamforming for Millimeter Wave Massive MIMO

Numerical results demonstrate that the proposed DT-assisted approach significantly reduces online interaction overhead by shifting the computational burden of ray-tracing to an offline generative phase, and achieves superior sum-rate performance and link robustness under dynamic blockages compared to conventional deep...

An Intelligent Bearing Fault Diagnosis Method Integrating Fault-Mechanism Priors and Graph State-Space Modeling

Artificial neural network-based analysis of triple-stratified viscoelastic nanofluid bioconvection flow with thermal radiation, chemical reaction, and suction/injection effects

Interpretable Deep Learning Reveals Hydro-Geomorphic Controls on Flood Susceptibility and Exposure in Data-Scarce Regions

Description This repository contains the source code, model architectures, and analysis workflows used in the study: “Interpretable Deep Learning Reveals Hydro-Geomorphic Controls on Flood Susceptibility and Exposure in Data-Scarce Regions” The study develops a geomorphology-driven flood susceptibility (FS) framework u...

Geometric and topological deep learning for predicting thermo-mechanical performance in cold spray deposition process modeling

This study presents a geometric deep learning framework for predicting cold spray particle impact responses using finite element simulation data. A parametric dataset was generated through automated Abaqus simulations spanning a systematic range of particle velocity, particle temperature, and friction coefficient, yiel...