为人工智能时代的代理模型制定统一报告标准，提升可复现性与跨领域应用能力。
SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era
- 提出代理模型报告标准（SMRS），规范数据采样、模型选择等关键环节
- 解决当前代理模型缺乏统一标准导致的可复现性差问题
- 适合科研人员、工程师及跨学科合作团队参考使用
代理模型广泛应用于科学与工程领域，以近似复杂系统从而降低计算成本。尽管应用广泛，该领域在建模流程的关键阶段——包括数据采样、模型选择、评估与下游分析——仍缺乏标准化，导致可复现性差且跨领域适用性受限，这一问题因人工智能驱动的代理模型快速普及而加剧。本文主张亟需建立结构化的报告标准——代理模型报告标准（SMRS），系统记录核心设计与评估决策，同时保持对实现细节的无关性。通过推广一种标准化但灵活的框架，旨在提升代理建模的可靠性，促进跨学科知识迁移，进而加速人工智能时代下的科学进步。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。