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Found 4 papers, 2 papers with code
Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement
The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts.
Deep Learning and Spectral Embedding for Graph Partitioning
The embedding phase is trained first by minimizing a loss function inspired by spectral graph theory.
Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks
The partitioning quality is compared with partitions obtained using METIS and SCOTCH, and the nested dissection ordering is evaluated in the sparse solver SuperLU.
A Study of Clustering Techniques and Hierarchical Matrix Formats for Kernel Ridge Regression
We present memory-efficient and scalable algorithms for kernel methods used in machine learning.
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