Caltech's neural operator predicts tipping points two years ahead with ~11-day error, in PNAS

AnimaAnandkumar · x · 2026-10-16

Anima Anandkumar's Caltech team published a PNAS paper on a Recurrent Neural Operator (RNO) that predicts tipping points before they happen.

• RNO tracks the history of system changes and outputs both the probability and timing of future tipping points — addressing the core challenge that tipping points appear suddenly.
• On simulated data, RNO predicted more than two years ahead of the expected tipping point, with a timing error of about 11 days.
• It also successfully predicted the stall transition in airflow simulations over a tilting wing, suggesting applications in aeronautical and aerospace design.
• Tipping-point detection builds on extensive prior work including a recent UN Science Board brief on climate modeling.

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