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Found 7 papers, 7 papers with code
Dirac--Bianconi Graph Neural Networks -- Enabling Non-Diffusive Long-Range Graph Predictions
The geometry of a graph is encoded in dynamical processes on the graph.
Domain-independent detection of known anomalies
In real-world use cases, two problems must be addressed: anomalous data is sparse and the same types of anomalies need to be detected on previously unseen objects.
Ranked #1 on
on Domain-independent anomalies datasets
Predicting Fault-Ride-Through Probability of Inverter-Dominated Power Grids using Machine Learning
Importantly, we demonstrate that ML models accurately predict the fault-ride-through probability of synthetic power grids.
Predicting Instability in Complex Oscillator Networks: Limitations and Potentials of Network Measures and Machine Learning
A functional property that is of theoretical and practical interest for oscillatory systems is the stability of synchrony to localized perturbations.
Towards dynamic stability analysis of sustainable power grids using graph neural networks
To mitigate climate change, the share of renewable needs to be increased.
Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networks
As a testbed for GNN models, we generate new, large datasets of dynamic stability of synthetic power grids, and provide them as an open-source resource to the research community.
Predicting Basin Stability of Power Grids using Graph Neural Networks
We investigate the feasibility of applying graph neural networks (GNN) to predict dynamic stability of synchronisation in complex power grids using the single-node basin stability (SNBS) as a measure.
