SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era
The standardization of surrogate models is significant for scientists and engineers across various domains, as it addresses a long-standing problem of reproducibility and knowledge transfer in the field.
The authors tackled the lack of standardization in surrogate models, proposing a unified reporting standard to improve reproducibility and cross-domain utility, with the goal of accelerating scientific progress. The proposed standard, SMRS, aims to systematically capture essential design and evaluation choices.
Surrogate models are widely used to approximate complex systems across science and engineering to reduce computational costs. Despite their widespread adoption, the field lacks standardisation across key stages of the modelling pipeline, including data sampling, model selection, evaluation, and downstream analysis. This fragmentation limits reproducibility and cross-domain utility -- a challenge further exacerbated by the rapid proliferation of AI-driven surrogate models. We argue for the urgent need to establish a structured reporting standard, the Surrogate Model Reporting Standard (SMRS), that systematically captures essential design and evaluation choices while remaining agnostic to implementation specifics. By promoting a standardised yet flexible framework, we aim to improve the reliability of surrogate modelling, foster interdisciplinary knowledge transfer, and, as a result, accelerate scientific progress in the AI era.