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arXiv AI By Hang Gao

From Manual Construction to AI-Driven Scenario Emergence: Rethinking Catastrophe Risk Modeling

Read the original on arXiv AI →

The article introduces the TAISE framework, which uses AI weather forecasting models to generate coherent extreme weather sequences at a fraction of the cost of traditional catastrophe risk models. By self‑iteratively producing continuous global atmospheric fields, TAISE captures temporal continuity and cross‑regional correlations that snapshot‑based methods miss. A proof‑of‑concept experiment shows an order‑of‑magnitude reduction in computational cost while maintaining key statistical properties of extreme events.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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