7 citations · 18 across the 9 of their papers we have counts for
5 papers · 1 filter
Dilated convolution neural operator for multiscale partial differential equations
Bo Xu, Xinliang Liu, Lei Zhang
This paper introduces a data-driven operator learning method for multiscale partial differential equations, with a particular emphasis on preserving high-frequency information. Dra…
Framelet Message Passing
Xinliang Liu, Bingxin Zhou, Chutian Zhang +1
Graph neural networks (GNNs) have achieved champion in wide applications. Neural message passing is a typical key module for feature propagation by aggregating neighboring features…
Mitigating spectral bias for the multiscale operator learning
Xinliang Liu, Bo Xu, Shuhao Cao +1
Neural operators have emerged as a powerful tool for learning the mapping between infinite-dimensional parameter and solution spaces of partial differential equations (PDEs). In th…
ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks
Yuelin Wang, Kai Yi, Xinliang Liu +2
Neural message passing is a basic feature extraction unit for graph-structured data considering neighboring node features in network propagation from one layer to the next. We mode…
Spectral Transform Forms Scalable Transformer
Bingxin Zhou, Xinliang Liu, Yuehua Liu +3
Many real-world relational systems, such as social networks and biological systems, contain dynamic interactions. When learning dynamic graph representation, it is essential to emp…