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arXiv Machine Learning By Amin Vakhshouri

A trust layer for fast pedestal-stability surrogates: calibrated error bars, out-of-distribution alarms and few-shot recalibration

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The paper presents a model‑agnostic trust layer for machine‑learning surrogates of linear peeling‑ballooning stability, adding calibrated error bars, out‑of‑distribution alarms, and few‑shot recalibration. Experiments on JET‑like equilibria, BOUT++ surrogates, and SPARC/DIII‑D data show that the trust layer maintains nominal coverage, flags unseen cases, and restores accuracy with minimal additional simulations. The approach enables rapid, reliable stability screening for new fusion device designs.

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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