An Analytical and AI-discovered Stable, Accurate, and Generalizable Subgrid-scale Closure for Geophysical Turbulence
This work addresses the challenge of unstable simulations in geophysical turbulence modeling, offering a more reliable method for researchers and practitioners in climate and fluid dynamics, though it is incremental as it builds on prior analytical and AI-based work.
The researchers tackled the problem of unstable large-eddy simulations in 2D geophysical turbulence by discovering a closed-form subgrid-scale closure using AI and fluid physics, resulting in accurate and stable simulations that reproduce DNS statistics, including extremes, with the closure derived from a 4th-order Taylor expansion.
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.