Search Results for author:
Found 6 papers, 2 papers with code
MgNO: Efficient Parameterization of Linear Operators via Multigrid
In this work, we propose a concise neural operator architecture for operator learning.
Mitigating spectral bias for the multiscale operator learning with hierarchical attention
Neural operators have emerged as a powerful tool for learning the mapping between infinite-dimensional parameter and solution spaces of partial differential equations (PDEs).
ACMP: Allen-Cahn Message Passing for Graph Neural Networks with Particle Phase Transition
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.
Spectral Transform Forms Scalable Transformer
The architecture is assembled with a few simple effective computational blocks that constitute randomized SVD, MLP, and graph Framelet convolution.
Generalized Rough Polyharmonic Splines for Multiscale PDEs with Rough Coefficients
In this paper, we demonstrate the construction of generalized Rough Polyhamronic Splines (GRPS) within the Bayesian framework, in particular, for multiscale PDEs with rough coefficients.
Numerical Analysis Numerical Analysis
