# Caltech team uses AI to forecast tipping points in simulations

> Researchers demonstrated a recurrent neural operator that predicts abrupt transitions in climate-system and aircraft-wing airflow simulations.

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- Section: Technology
- Country: United States
- Published: 2026-10-16T10:36:23+00:00
- Updated: 2026-10-16T10:36:23+00:00
- Event / data period: 2026-10-15
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Researchers led by Caltech reported on 15 October that a deep-learning model called a recurrent neural operator could forecast tipping points in simulations of simple systems. Tests covered a climate-change system and turbulent airflow over an aircraft wing; the study appeared in PNAS.

The team proposes using the model’s detection that learned physics no longer describes the dynamics as a warning of an approaching abrupt transition. The results are from simulations and do not establish a forecasting system validated in the real climate or in flight. The research involved scientists from Caltech, the University of Colorado Boulder and NVIDIA.

## Data

### Research record

Research announcement published 15 October

Methodology: Findings are simulation results reported in the PNAS paper and Caltech account; they do not show operational forecasting performance in the real world.

| date | label | description | status |
|---|---|---|---|
| 2026-10-15 | Neural-operator simulations | Forecasting transitions in climate-system and aircraft-wing airflow simulations. | occurred |

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## Sources

- [Caltech](<https://www.caltech.edu/about/news/forecasting-tipping-points-with-ai>) (Source checked: 2026-10-16; Published: 2026-10-15)

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