An Analytical and AI-discovered Stable, Accurate, and Generalizable Subgrid-scale Closure for Geophysical Turbulence
Atmospheric and Oceanic Physics
2026-01-21 v3 Machine Learning
Abstract
By combining AI and fluid physics, we discover a closed-form closure for 2D turbulence from small direct numerical simulation (DNS) data. Large-eddy simulation (LES) with this closure is accurate and stable, reproducing DNS statistics including those of extremes. We also show that the new closure could be derived from a 4th-order truncated Taylor expansion. Prior analytical and AI-based work only found the 2nd-order expansion, which led to unstable LES. The additional terms emerge only when inter-scale energy transfer is considered alongside standard reconstruction criterion in the sparse-equation discovery.
Keywords
Cite
@article{arxiv.2509.20365,
title = {An Analytical and AI-discovered Stable, Accurate, and Generalizable Subgrid-scale Closure for Geophysical Turbulence},
author = {Karan Jakhar and Yifei Guan and Pedram Hassanzadeh},
journal= {arXiv preprint arXiv:2509.20365},
year = {2026}
}
Comments
Main manuscript: 6 pages, 3 figures; End Matter: 1 page, 1 figure; Supplementary Information: 7 pages, 5 figures, 2 tables