为科学机器学习建立可信建模规范，提升预测可靠性。
Verification and Validation for Trustworthy Scientific Machine Learning
- 提出16条建议，解决现有验证流程在科学机器学习中的适用难题。
- 聚焦预测性模型，强调模型开发与文档的严谨性。
- 适合科研人员、模型开发者及审稿人参考，提升研究可重复性。
科学机器学习（SciML）正在改变多个科学领域，但其可信建模实践的发展滞后于应用，限制了实际影响力。本文旨在推动建立预测性科学机器学习的共识性良好实践。我们识别出现有计算科学与工程验证与确认协议在应用中的关键挑战，并提出16项具体建议以应对这些问题。讨论重点在于利用机器学习模型学习、改进和加速物理系统数值模拟的预测性应用场景。尽管聚焦预测性应用，这些建议旨在帮助研究人员在所有科学机器学习领域中更严谨地开展并记录建模过程。
原文摘要 · Abstract (English)
Scientific machine learning (SciML) models are transforming many scientific disciplines. However, the development of good modeling practices to increase the trustworthiness of SciML has lagged behind its application, limiting its potential impact. The goal of this paper is to start a discussion on establishing consensus-based good practices for predictive SciML. We identify key challenges in applying existing computational science and engineering guidelines, such as verification and validation protocols, and provide recommendations to address these challenges. Our discussion focuses on predictive SciML, which uses machine learning models to learn, improve, and accelerate numerical simulations of physical systems. While centered on predictive applications, our 16 recommendations aim to help researchers conduct and document their modeling processes rigorously across all SciML domains.
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