SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era

TL;DR

This paper proposes the Surrogate Model Reporting Standard (SMRS), a structured framework to standardise reporting practices in surrogate modelling, enhancing reproducibility, reliability, and interdisciplinary collaboration in AI-driven scientific research.

Contribution

The paper introduces SMRS, a comprehensive reporting standard for surrogate models that addresses current fragmentation and promotes consistency across the modelling pipeline.

Findings

SMRS improves reproducibility of surrogate models.

Standardised reporting facilitates cross-domain knowledge transfer.

Adoption of SMRS accelerates scientific progress in AI applications.

Abstract

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.