Domain-Validity-Gated Metamorphic Testing of Scientific ML Surrogates
Scientific machine-learning (SciML) surrogates approximate expensive simulations, but exact expected outputs for arbitrary inputs are unavailable (the oracle problem). Metamorphic testing checks relations across executions, yet a candidate relation is not automatically valid: its preconditions, output mapping, and the numerical floor of the scoring operator determine whether a violation is meaningful. We study how candidate metamorphic relations (MRs) can be screened for domain validity and turned into executable, oracle-free test assets for SciML surrogates. We propose (i) a domain-validity rubric that admits a candidate only when its tolerance dominates the operator's numerical floor an...
| Authors | Meng Li, Xiaohua Yang, Jie Liu et al. |
|---|---|
| Primary category | cs.CE |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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