用物理信息提升模型效率，降低结构监测的碳排放。
Green Physics-Informed Machine Learning Models For Structural Health Monitoring
- 融合物理规律与数据驱动，构建更高效的灰箱模型
- 灰箱模型运行更快，碳排放显著低于纯数据模型
- 适合关注可持续性的结构健康监测研究者
机器学习在结构工程和结构健康监测中日益重要，因其能快速准确完成回归与分类任务。然而纯数据驱动方法在缺乏环境与工况数据时存在局限，促使了物理信息机器学习模型的发展。这类‘灰箱’模型结合工程师对结构的物理认知，在多个领域表现优异。本文从‘绿色’视角比较黑箱与灰箱模型的环境影响，发现灰箱模型出色的外推性能可减少运行时间，从而降低碳排放。作者通过结构健康监测案例验证：在保持高性能的同时，可显著降低计算成本，实现更可持续的建模方案。
原文摘要 · Abstract (English)
Machine learning continues to emerge as an important tool to be utilised within structural engineering and structural health monitoring, due to its ability to accurately and quickly perform both regression and classification tasks. However, a purely data driven approach has its limitations, particularly where we lack data from relevant environmental and operational conditions, a situation that has led to the development of physics-informed machine learners for structural health monitoring. These "grey-box" models take into account the physical insight that an engineer would have about the structure they are modelling and have shown promising results in the structural engineering field among many others. This work compares black and grey-box models through a "green" lens, comparing them in terms of their environmental impact, and investigating how the high extrapolative performance of grey-box models can reduce their runtimes and therefore carbon emissions. The authors aim to develop physics-informed models with reduced computational costs, while maintaining high performance, illustrated through a structural health monitoring case study.
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