SOURCE-LINKED INTELLIGENCE
From Graphs to Feeders: Constraint-Guided Diffusion for Rule-Compliant Feeder Generation
Generative modeling approaches often focus on recovering broad statistical characteristics from the training data. In the context of graph generation, this may refer to degree distributions, clustering coefficients, or spectral properties. However, generating usable distribution feeders when detailed feeder models are unavailable requires more than matching generic graph statistics: the sampled topology must also obey electrical compatibility and radiality rules. We therefore formulate feeder synthesis as a constraint-guided graph generation problem and propose the Power-Grid-constrained Discr
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- arXiv · AI, language, vision and robotics · 2026-09-24T14:31:12.000Z
First collected: 2026-09-25T06:12:46.948Z. This is not the publication date.