LEAP ML Journal Club

Jakhar, K., Guan, Y., and Hassanzadeh, P. (2026). Analytical and AI-Discovered Stable, Accurate, and Generalizable Subgrid-Scale Closure for Geophysical Turbulence. APS Physical Review Letters, vol. 136, no. 6, Art. no. 064201. doi:10.1103/v28b-5qmp.

A light lunch will be served for in-person attendees.

*Please note that in-person space is limited.*

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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.

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Columbia Engineering Innovation Hub, 2276 12th Avenue, New York, United States

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