6 citations · 8 across the 7 of their papers we have counts for
10 papers
Learning coarse-step dynamics and internal mechanical response with graph networks
Vinay Sharma, Olga Fink
Modern sensing records the motion of physical systems, but often leaves the forces and mechanical response governing that motion unobserved. Inferring these quantities from discret…
WaveGraphNet: Physics-Consistent Guided-Wave Damage Localization through Coupled Inverse-Forward Graph Learning
Vinay Sharma, Aditya Bharade, Olga Fink
Guided-wave structural health monitoring enables damage localization in composite plates using sparse networks of bonded piezoelectric transducers. However, supervised localization…
Virtual Smart Metering in District Heating Networks via Heterogeneous Spatial-Temporal Graph Neural Networks
Keivan Faghih Niresi, Christian Møller Jensen, Carsten Skovmose Kallesøe +2
Intelligent operation of thermal energy networks aims to improve energy efficiency, reliability, and operational flexibility through data-driven control, predictive optimization, a…
Disentangling Slow and Fast Temporal Dynamics in Degradation Inference with Hierarchical Differential Models
Mengjie Zhao, Olga Fink
Reliable inference of system degradation from sensor data is fundamental to condition monitoring and prognostics in mechanical and infrastructural systems. Since degradation is rar…
Equi-Euler GraphNet: An Equivariant, Temporal-Dynamics Informed Graph Neural Network for Dual Force and Trajectory Prediction in Multi-Body Systems
Vinay Sharma, Rémi Tanguy Oddon, Pietro Tesini +3
Accurate real-time modeling of multi-body dynamical systems is essential for enabling digital twin applications across industries. While many data-driven approaches aim to learn sy…
Dynami-CAL GraphNet: A Physics-Informed Graph Neural Network Conserving Linear and Angular Momentum for Dynamical Systems
Vinay Sharma, Olga Fink
Accurate, interpretable, and real-time modeling of multi-body dynamical systems is essential for predicting behaviors and inferring physical properties in natural and engineered en…
Graph Neural Networks for Virtual Sensing in Complex Systems: Addressing Heterogeneous Temporal Dynamics
Mengjie Zhao, Cees Taal, Stephan Baggerohr +1
Real-time condition monitoring is crucial for the reliable and efficient operation of complex systems. However, relying solely on physical sensors can be limited due to their cost,…
Virtual Sensor for Real-Time Bearing Load Prediction Using Heterogeneous Temporal Graph Neural Networks
Mengjie Zhao, Cees Taal, Stephan Baggerohr +1
Accurate bearing load monitoring is essential for their Prognostics and Health Management (PHM), enabling damage assessment, wear prediction, and proactive maintenance. While beari…
Graph Neural Networks for Dynamic Modeling of Roller Bearing
Vinay Sharma, Jens Ravesloot, Cees Taal +1
In the presented work, we propose to apply the framework of graph neural networks (GNNs) to predict the dynamics of a rolling element bearing. This approach offers generalizability…
Domain Adaptation via Alignment of Operation Profile for Remaining Useful Lifetime Prediction
Ismail Nejjar, Fabian Geissmann, Mengjie Zhao +2
Effective Prognostics and Health Management (PHM) relies on accurate prediction of the Remaining Useful Life (RUL). Data-driven RUL prediction techniques rely heavily on the repres…