Abstract: In this study, physics-informed graph residual learning (PhiGRL) is proposed as an effective and robust deep learning (DL)-based approach for 3-D electromagnetic (EM) modeling. Extended from ...
Abstract: A seminal work of [Ahn-Guha-McGregor, PODS’12] showed that one can compute a cut sparsifier of an unweighted undirected graph by taking a near-linear number of linear measurements on the ...
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