FTD-GNO: Memory-Efficient Graph Neural Operators through Functional Tensor Decomposition of the Kernel

Excerpt

arXiv:2610.04212v1 Announce Type: cross Abstract: Graph Neural Operators (GNOs) provide flexible surrogate models for learning solution operators of partial differential equations (PDEs). However, standard GNOs typically parameterize the integral kernel with a monolithic neural network and evaluate kernel interactions over graph edges, leading to substantial computational and memory overhead at high resolutions or with large neighborhoods. To address these limitations, we propose Functional Tens