37 citations · 69 across the 9 of their papers we have counts for
11 papers
WaveGSSM: Graph Wave State Space Models for Propagating Spatio-Temporal Patterns
Junyou Zhu, Fenying Cai, Ping Xiong +5
Spatio-temporal graph models typically encode each snapshot with a GNN and then connect the resulting representations through a temporal module. This space-then-time design is effe…
Stability-Shaped Deep Graph Learning
Junyou Zhu, Langzhou He, Fenying Cai +7
In deep graph neural networks, increasing depth enlarges the receptive field but often leads to over-smoothing, where node representations tend to align. We develop a unified, mode…
Graph Machine Learning: An Opportunity for Power Systems
Martin Sadric, Sebastian Pütz, Christian Nauck +4
Modern power systems face growing operational complexity driven by the integration of renewable energy sources, decentralization, and the need for real-time decision-making across…
Learning Dynamic Stability Landscapes in Synchronization Networks
Christian Nauck, Junyou Zhu, Michael Lindner +1
The robustness of synchronization is typically characterized by scalar, per-node stability indices whose dependence on topology is studied via network science or graph neural netwo…
Dirac--Bianconi Graph Neural Networks -- Enabling Non-Diffusive Long-Range Graph Predictions
Christian Nauck, Rohan Gorantla, Michael Lindner +3
The geometry of a graph is encoded in dynamical processes on the graph. Many graph neural network (GNN) architectures are inspired by such dynamical systems, typically based on the…
Domain-independent detection of known anomalies
Jonas Bühler, Jonas Fehrenbach, Lucas Steinmann +2
One persistent obstacle in industrial quality inspection is the detection of anomalies. In real-world use cases, two problems must be addressed: anomalous data is sparse and the sa…
Predicting Fault-Ride-Through Probability of Inverter-Dominated Power Grids using Machine Learning
Christian Nauck, Anna Büttner, Sebastian Liemann +2
Due to the increasing share of renewables, the analysis of the dynamical behavior of power grids gains importance. Effective risk assessments necessitate the analysis of large numb…
Instability in Complex Oscillator Networks: Limitations and Potentials of Network Measures and Machine Learning
Christian Nauck, Michael Lindner, Nora Molkenthin +4
A central question of network science is how functional properties of systems emerge from their structure. For networked dynamical systems, structure is typically captured through…
Towards dynamic stability analysis of sustainable power grids using graph neural networks
Christian Nauck, Michael Lindner, Konstantin Schürholt +1
To mitigate climate change, the share of renewable needs to be increased. Renewable energies introduce new challenges to power grids due to decentralization, reduced inertia and vo…
Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networks
Christian Nauck, Michael Lindner, Konstantin Schürholt +1
To mitigate climate change, the share of renewable energies in power production needs to be increased. Renewables introduce new challenges to power grids regarding the dynamic stab…
Predicting Basin Stability of Power Grids using Graph Neural Networks
Christian Nauck, Michael Lindner, Konstantin Schürholt +5
The prediction of dynamical stability of power grids becomes more important and challenging with increasing shares of renewable energy sources due to their decentralized structure,…