SCoReT: Super-Resolution Compression and Reconstruction of Turbulent Flows

Abstract

High-fidelity simulations of the Navier--Stokes equations (NSE) generate massive amounts of data, motivating the need for efficient compression and reconstruction strategies for turbulent flows. At the same time, reconstructing flow fields from sparse measurements while retaining spectral content, turbulence statistics, and coherent structures remains a major challenge. This work investigates two complementary paradigms for turbulent flow reconstruction: supervised reconstruction and physics-informed reconstruction, in the context of transition to turbulence induced by three-dimensional distributed roughness elements. We introduce a vorticity-augmented supervised approach and a physics-informed approach, implemented through a partially assisted compressible PINN formulation based on the three-dimensional unsteady compressible Navier--Stokes equations. Reconstruction performance is evaluated at different sparsity levels using instantaneous velocity fields, mean-squared error, energy spectra, Reynolds stresses, turbulent kinetic energy, and Q-criterion isosurfaces. Rather than establishing a universal winner, the present study characterises the respective strengths, limitations, and operating regimes of these two approaches. The results indicate that at lower sparsity levels, the vorticity-augmented supervised model yields the lowest reconstruction error, recovers key statistical and spectral features, and enables substantial data compression. The PINN shows potential to reconstruct turbulent flows from sparse measurements without high-resolution labels and exhibits comparatively stable held-out extrapolation behaviour at higher sparsity. These results suggest the potential of combining data-driven and physics-informed learning for flow data compression and physics-informed reconstruction of turbulent flows from sparse data.