FLUIDSPLAT: Reconstructing Physical Fields from Sparse Sensors via Gaussian Primitives
The paper titled FLUIDSPLAT presents a new model for reconstructing physical fields from sparse sensor data. This model, inspired by 3D Gaussian Splatting, offers a more interpretable representation of flow fields. The authors demonstrate that FLUIDSPLAT outperforms existing methods in accuracy on standard benchmarks.
- ▪FLUIDSPLAT is designed to reconstruct continuous flow fields from sparse surface-mounted sensors.
- ▪The model predicts anisotropic Gaussian primitives, providing a spatially explicit representation of the flow.
- ▪On a standard cylinder-flow benchmark, FLUIDSPLAT achieves the best mean error across all surface-sensor layouts.
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Computer Science > Machine Learning arXiv:2605.18866 (cs) [Submitted on 15 May 2026] Title:FLUIDSPLAT: Reconstructing Physical Fields from Sparse Sensors via Gaussian Primitives Authors:Huaxi Huang, Meng Li, Zhengqing Gao, Xi Zhou, Xiaoshui Huang, Xiao Sun View a PDF of the paper titled FLUIDSPLAT: Reconstructing Physical Fields from Sparse Sensors via Gaussian Primitives, by Huaxi Huang and 5 other authors View PDF HTML (experimental) Abstract:Reconstructing continuous flow fields from sparse surface-mounted sensors is central to aerodynamic design, flow control, and digital-twin instrumentation.
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