FLUIDSPLAT uses K anisotropic Gaussian primitives as an interpretable scaffold for flow-field reconstruction, proves an O(K^{-s/d}) approximation rate under Sobolev smoothness s, derives an optimal K scaling with observation count N and noise level, and reports 11-23% error reduction on AirfRANS.
Phy- sense: Sensor placement optimization for accurate physics sensing.arXiv preprint arXiv:2505.18190
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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 uses K anisotropic Gaussian primitives as an interpretable scaffold for flow-field reconstruction, proves an O(K^{-s/d}) approximation rate under Sobolev smoothness s, derives an optimal K scaling with observation count N and noise level, and reports 11-23% error reduction on AirfRANS.
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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.