English(EN) From Grey-Box to Green-Box: When can Physics-Informed Machine Learning Reduce Carbon Footprints in Structural Health Monitoring?

物理信息机器学习可能减少结构监测中的碳足迹

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

一篇新的研究论文探讨了物理信息机器学习（PIML）在结构健康监测中的环境影响。该研究将四种 PIML 方法的碳足迹与传统的黑盒模型进行了比较，发现 PIML 通常需要更少的数据和训练时间，从而减少了排放。然而，该研究强调了 PIML 的附加复杂性与其环境效益之间的权衡，并建议需要仔细考虑才能实现最佳应用。 AI

影响这项研究表明，采用物理信息机器学习可以通过减少与模型训练相关的碳足迹，从而实现更可持续的人工智能发展。

排序理由该集群包含一篇详细介绍新颖发现和方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Daisy R. Bradley, Nathan A. Hinchliffe, Daniel J. Pitchforth, Matthew R. Jones, Elizabeth J. Cross · 2026-11-05 05:00

从灰盒到绿盒：物理信息机器学习何时能减少结构健康监测中的碳足迹？

arXiv:2609.33387v2 Announce Type: replace Abstract: Machine learning plays an increasingly vital role in engineering, but the corresponding increase in compute time is not without environmental cost. Physics-informed machine learning or "grey-box" models have been developed to ov…

本文如何被排名

报道来源 [1]

从灰盒到绿盒：物理信息机器学习何时能减少结构健康监测中的碳足迹？

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