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