SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914108393127936 |
|---|---|
| author | Semenova, Elizaveta Sheinkman, Alisa Hitge, Timothy James Hall, Siobhan Mackenzie Cockayne, Jon |
| author_facet | Semenova, Elizaveta Sheinkman, Alisa Hitge, Timothy James Hall, Siobhan Mackenzie Cockayne, Jon |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_06753 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era Semenova, Elizaveta Sheinkman, Alisa Hitge, Timothy James Hall, Siobhan Mackenzie Cockayne, Jon Computation Machine Learning 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. |
| title | SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era |
| topic | Computation Machine Learning |
| url | https://arxiv.org/abs/2502.06753 |