SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era.
Elizaveta Semenova, Siobhan Mackenzie Hall, Timothy James Hitge, Alisa Sheinkman, Jon Cockayne
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.
BibTeX
@inproceedings{
semenova2025smrs,
title={{SMRS}: advocating a unified reporting standard for surrogate models in the artificial intelligence era.},
author={Elizaveta Semenova and Siobhan Mackenzie Hall and Timothy James Hitge and Alisa Sheinkman and Jon Cockayne},
booktitle={The Thirty-Ninth Annual Conference on Neural Information Processing Systems Position Paper Track},
year={2025},
url={https://openreview.net/forum?id=V6DcL5L6CU}
}