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arXiv Machine Learning By Isabella S. Thiel, Juan Bello-Rivas, Yannis G. Kevrekidis, Themistoklis P. Sapsis

Mechanism-Aware Ensemble Conditioning for Data-Limited Emulation of Extreme Events

Read the original on arXiv Machine Learning →

The paper introduces a mechanism‑aware conditioning framework that uses a nudged coarse ensemble to capture local instability geometry in chaotic systems. By injecting ensemble covariance statistics via a small FiLM module, the authors enhance rare‑event emulation in both a low‑dimensional chaotic benchmark and a quasi‑geostrophic flow model, achieving significant improvements in exceedance‑frequency and tail‑density errors with limited data. The approach demonstrates that local instability information can be leveraged as a practical conditioning signal for data‑efficient emulation 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 Machine Learning.

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