AI-based separation of turbulence from coherent background flows in decaying hydrodynamic turbulence

TL;DR

This paper demonstrates that AI models trained on static images can effectively separate turbulence from background flows in decaying hydrodynamic turbulence simulations, even as turbulence weakens over time.

Contribution

It introduces a neural network approach trained on synthetic static images that robustly separates turbulent fluctuations from background flows in evolving turbulence simulations.

Findings

Successful turbulence separation during early and intermediate stages.

Model maintains plausible turbulence structures at late stages.

Preserves inertial-range spectral scaling in recovered turbulence.

Abstract

Separating turbulent fluctuations from coherent large-scale background flows is a fundamental challenge in the analysis of numerical simulations and astronomical observations. Traditional approaches to this problem commonly rely on decomposition-based techniques, including scale-based filtering methods such as Fourier or wavelet transforms, as well as adaptive methods like the Hilbert-Huang transformation. In realistic flows, however, coherent motions and turbulent fluctuations often overlap across a broad range of scales and interact nonlinearly, rendering any clear and unique separation inherently ambiguous, particularly in astrophysical settings where data are projected or sparsely sampled. In this work, we assess the robustness of AI-based turbulence-background separation using two-dimensional incompressible Navier-Stokes simulations of decaying hydrodynamic turbulence. The simulations are initialized with a coherent background flow and divergence-free turbulent perturbations with a Kolmogorov-like power spectrum, and evolve without external forcing, providing a conservative physical testbed. A neural network trained exclusively on static synthetic images is applied to simulation snapshots at different evolutionary stages. We find that the model successfully recovers turbulent fluctuations during early and intermediate stages, when partial scale separation is preserved. At later stages, despite the substantial decay of turbulent energy and the resulting reduction in fluctuation strength, the model continues to recover visually and spectrally plausible turbulent structures and preserves inertial-range spectral scaling, demonstrating robust separation under increasingly challenging conditions. These results demonstrate the potential of applying AI models trained on static data to time-evolving turbulent flows, with direct implications for astrophysical and cosmological applications.