2026-02-11 シカゴ大学（UChicago）
The turbulent swirling of fluids, including water and air, has remained stubbornly difficult for scientists to model computationally. A new study shows how AI models could be used to tackle this and other scientific questions.Photo copyright Shutterstock
＜関連情報＞
- https://news.uchicago.edu/story/scientists-pair-ai-and-human-knowledge-tackle-notoriously-difficult-physics-question
- https://journals.aps.org/prl/abstract/10.1103/v28b-5qmp
地球物理学的乱流に対する解析的かつAIによって発見された、安定的、正確かつ一般化可能なサブグリッドスケール閉包 Analytical and AI-Discovered Stable, Accurate, and Generalizable Subgrid-Scale Closure for Geophysical Turbulence
Karan Jakhar, Yifei Guan, and Pedram Hassanzadeh
Physical Review Letters Published: 10 February, 2026
DOI: https://doi.org/10.1103/v28b-5qmp
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.
