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Check out the paper led by PhD student Karan Jakhar titled “Analytical and AI-discovered stable, accurate, and generalizable subgrid-scale closure for geophysical turbulence” published in PRL. The paper shows that AI, specifically techniques that can discover equations from data, can identify new closure models for turbulence that are accurate and stable, and work for unseen flows, but only after physics constraints are included in the discovery. The paper also shows that guided by the AI discovery, this closure could be derived analytically. The results provide an example of AI accelerating scientific discovery. The paper is highlighted by APS Editors and by UChicago News.