Reformulating Neural Operators in $d+1$ Dimensions for Embedding Evolution 事件

Reformulating Neural Operators in $d+1$ Dimensions for Embedding Evolution 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 remains underexplored, which often drives models toward computationally expensive embedding-scaling designs to improve approximat