Work

Analytical and AI-Discovered Stable, Accurate, and Generalizable Subgrid-Scale Closure for Geophysical Turbulence

Year: 2025

Type: article

Abstract: By combining artificial intelligence and fluid physics, we discover a closed-form closure for 2D turbulence from small direct numerical simulation data. Large-eddy simulation with this closure is accurate and stable, reproducing direct numerical simulation statistics, including those of extremes. We also show that the new closure could be derived from a fourth-order truncated Taylor expansion. Prior analytical and artificial-intelligence-based work only found the second-order expansion, which led to unstable large-eddy simulation. The additional terms emerge only when interscale energy transfer is considered alongside standard reconstruction criterion in the sparse-equation discovery.

Source: Physical Review Letters

DOI: 10.1103/v28b-5qmp

Authors: Karan Jakhar, Yifei Guan, Pedram Hassanzadeh

Institutions: University of Chicago, Rice University, Union College

Language: English

Topic: Fluid Dynamics and Turbulent Flows

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, National Center for Atmospheric Research, Computational and Information Systems Laboratory

Awards: G1103549601, G5781760129, CAREER: Quantifying the Dynamics and Spatiotemporal Variability of Blocking Events Using Linear Response Functions and the Buckingham-Pi Theorem