AI RESEARCH
Compositional Neural Operators for Multi-Dimensional Fluid Dynamics
arXiv CS.LG
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ArXi:2605.11691v1 Announce Type: new Partial differential equations (PDEs) govern diverse physical phenomena, yet high-fidelity numerical solutions are computationally expensive and Machine Learning approaches lack generalization. While Scientific Foundation Models (SFMs) aim to provide universal surrogates, typical encoding-decoding approaches suffer from high pre