SMRS: Advocating a Unified Reporting Standard for Surrogate Models in the Artificial Intelligence Era.
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.
Cite
Text
Semenova et al. "SMRS: Advocating a Unified Reporting Standard for Surrogate Models in the Artificial Intelligence Era.." Advances in Neural Information Processing Systems, 2025.Markdown
[Semenova et al. "SMRS: Advocating a Unified Reporting Standard for Surrogate Models in the Artificial Intelligence Era.." Advances in Neural Information Processing Systems, 2025.](https://mlanthology.org/neurips/2025/semenova2025neurips-smrs/)BibTeX
@inproceedings{semenova2025neurips-smrs,
title = {{SMRS: Advocating a Unified Reporting Standard for Surrogate Models in the Artificial Intelligence Era.}},
author = {Semenova, Elizaveta and Hall, Siobhan Mackenzie and Hitge, Timothy James and Sheinkman, Alisa and Cockayne, Jon},
booktitle = {Advances in Neural Information Processing Systems},
year = {2025},
url = {https://mlanthology.org/neurips/2025/semenova2025neurips-smrs/}
}