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This paper presents principled approaches for converting popular neural network architectures (CNNs, GNNs, transformers) into neural operators that learn mappings between infinite-dimensional function spaces, enabling consistent predictions across different discretizations for scientific modeling. Published in Nature Machine Intelligence.