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Gaussian field representations for turbulent flow: Compression, scale separation, and physical fidelity
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DOI：10.1016/j.compfluid.2026.107202.png)
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Abstract
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Representing turbulent flow fields in a compact yet physically faithful form remains a central challenge in computational fluid dynamics. We propose a continuous parametric representation based on localized Gaussian primitives, in which the velocity field is modeled as a superposition of kernels with learnable positions, amplitudes, and characteristic scales. This formulation yields a compact, grid-independent encoding while enabling analytical evaluation of derived quantities such as vorticity and enstrophy.
Keywords:
Turbulence
Flow field compression
Gaussian basis functions
Reduced-order modeling
Energy spectrum
Enstrophy
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