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Check out the paper co-led by ENS-Paris PhD student Amaury Lancelin and UChicago postdoc Dr. Alex Wikner titled “AI-boosted rare event sampling to characterize extreme weather” published in PRL. The rarest weather is the hardest to simulate and characterize, because it almost never happens: learning about a once-in-a-thousand-year heat wave would require simulating thousands of years of weather. The paper offers a hybrid AI+math+physics solution: a fast AI weather model guides an expensive physics-based climate model, through a mathematical algorithm, to more effciently sample times where the chance of extreme events is higher. This reproduces the statistics of heat waves with 100x less computing time, making it practical to study the most extreme (and impactful) events that have not happened yet, the “gray swans”. The paper is highlighted by APS Editors and by UChicago News.