arXiv Machine Learning June 17, 2026 By Meng Li, Xiaohua Yang, Jie Liu, Shiyu Yan

Domain-Validity-Gated Metamorphic Testing of Scientific ML Surrogates

Read the original on arXiv Machine Learning →

arXiv:2606. 17529v1 Announce Type: cross Abstract: Scientific machine-learning (SciML) surrogates approximate expensive simulations, but exact expected outputs for arbitrary inputs are unavailable (the oracle problem).

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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