FLUIDSPLAT models flow fields with K anisotropic Gaussian primitives, proves an O(K^{-s/d}) approximation rate under Sobolev smoothness s, derives optimal K scaling with N sensors, and reports 11-28% error reduction on four flow benchmarks.
arXiv preprint arXiv:2505.18190 , year=
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PerFlow decouples observation conditioning from physics enforcement in rectified flows using constraint-preserving projections and invariance guarantees for fast, physics-consistent reconstruction of spatiotemporal dynamics.
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FLUIDSPLAT: Reconstructing Physical Fields from Sparse Sensors via Gaussian Primitives
FLUIDSPLAT models flow fields with K anisotropic Gaussian primitives, proves an O(K^{-s/d}) approximation rate under Sobolev smoothness s, derives optimal K scaling with N sensors, and reports 11-28% error reduction on four flow benchmarks.
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PerFlow: Physics-Embedded Rectified Flow for Efficient Reconstruction and Uncertainty Quantification of Spatiotemporal Dynamics
PerFlow decouples observation conditioning from physics enforcement in rectified flows using constraint-preserving projections and invariance guarantees for fast, physics-consistent reconstruction of spatiotemporal dynamics.