发表机构
University of Sheffield（谢菲尔德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究在结构健康监测中评估四种物理信息机器学习方法，发现多数方法相比黑盒模型能减少训练碳排放，但需权衡模型复杂度与数据需求。
AI 中文摘要
机器学习在工程中发挥着日益重要的作用，但相应的计算时间增加并非没有环境代价。物理信息机器学习或“灰盒”模型已被开发出来，以克服传统黑盒学习器的一些局限性，利用工程师对其所建模结构的物理洞察，并在结构工程等领域展现出有前景的结果。本研究探讨是否还存在减少环境影响这一额外优势，考虑训练数据数量与训练时间之间的关系，并将这一时长与计算产生的碳排放联系起来。在结构健康监测背景下，评估了四种物理信息机器学习方法——涵盖高斯过程和神经网络：残差建模、输入增强、混合建模和约束学习。比较了每种模型达到给定误差阈值所需的训练排放量，在大多数示例中，物理信息模型的排放量更低（输入增强模型是个例外）。这种训练排放的减少进一步增加了通过收集和存储更少数据所实现的环境节约。尽管结果令人鼓舞，但我们不能期望灵丹妙药，案例研究表明，需要在将物理引入机器学习器所带来的增加复杂度与减少训练数据需求所带来的收益之间进行权衡。
英文摘要
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 overcome some of the limitations of traditional black-box learners, utilising 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 explores whether an additional advantage could be a reduced environmental impact, considering the relationship between training data quantity and training time, linking this duration to carbon emissions from computing. In a structural health monitoring context, four physics-informed machine learning approaches - spanning Gaussian processes and neural networks - are evaluated: residual modelling, input augmentation, hybrid modelling, and constrained learning. The emissions for training each of the models to reach a given error threshold is compared, and in most examples, shown to be lower for the physics-informed models (with input augmented models being an exception). This reduction in training emissions further compounds the environmental savings achieved by collecting and storing less data. Although promising results, we cannot expect a silver bullet and the case studies demonstrate that a trade-off is needed between the increased complexity that comes from introducing physics into a machine learner, against the gain from reduced training data requirements.
Comments25 pages, 15 figures
