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stat.ML2022
A comparison of PINN approaches for drift-diffusion equations on metric graphs
Jan Blechschmidt, Jan-Frederik Pietschman, Tom-Christian Riemer +2
In this paper we focus on comparing machine learning approaches for quantum graphs, which are metric graphs, i.e., graphs with dedicated edge lengths, and an associated differentia…
stat.ML2021
RaWaNet: Enriching Graph Neural Network Input via Random Walks on Graphs
Anahita Iravanizad, Edgar Ivan Sanchez Medina, Martin Stoll
In recent years, graph neural networks (GNNs) have gained increasing popularity and have shown very promising results for data that are represented by graphs. The majority of GNN a…