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News/Reformulating Neural Operators in $d+1$ Dimensions for Embedding Evolution
arxiv
PublishedJune 6, 2026 at 4:00 AM
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Reformulating Neural Operators in $d+1$ Dimensions for Embedding Evolution

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arXiv:2505.11766v4 Announce Type: replace-cross Abstract: Neural Operators (NOs) are powerful architectures for learning mappings between function spaces. While most advances focus on refining kernel parameterizations over the $d$-dimensional physical domain, the evolution of lifted embeddings remai

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