# Multipole Graph Neural Operator for Parametric Partial Differential Equations

@article{Li2020MultipoleGN, title={Multipole Graph Neural Operator for Parametric Partial Differential Equations}, author={Zong-Yi Li and Nikola B. Kovachki and Kamyar Azizzadenesheli and Burigede Liu and Kaushik Bhattacharya and Andrew Stuart and Anima Anandkumar}, journal={ArXiv}, year={2020}, volume={abs/2006.09535} }

One of the main challenges in using deep learning-based methods for simulating physical systems and solving partial differential equations (PDEs) is formulating physics-based data in the desired structure for neural networks. Graph neural networks (GNNs) have gained popularity in this area since graphs offer a natural way of modeling particle interactions and provide a clear way of discretizing the continuum models. However, the graphs constructed for approximating such tasks usually ignore…

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