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A brief overview of PDEs on graphs

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Recent years have seen a lot of interest in partial differential equations discretised on arbitrary network structures. Especially driven by applications in data analysis and image processing, models such as the graph-based Ginzburg-Landau functional and variations on it have been used for processes such as data clustering, image segmentation, and maximum-cut computations. In this talk I will give a brief overview of some of the theory and applications in this area.

This talk is part of the Applied and Computational Analysis series.

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