Physics-Inspired Spatial-Temporal Graph Neural Networks for Predicting Industrial Chain Resilience

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	Physics-Inspired Spatial-Temporal Graph Neural Networks for Predicting Industrial Chain Resilience
	Wang BC(王必成); Wang JP(王军平)
刊名	Journal of Machine Learning Research
	2024-04
卷号	4 期号:23 页码:1-23
英文摘要	Industrial chains are playing an increasingly important role in the sustainable development of the national economy. However, the precise prediction of the dynamics of industrial chain evolution has become a significant challenge in the field of machine learning. Currently, in the aspect of machine learning solving system dynamics predictionsome successful examples have been achieved in this field, such as PINNs (Physics-Informed Neural Networks). However, machine learning methods, represented by PINNs, in describing and analyzing the dynamical behavior of industrial chain resilience, is still in its infancy. At its core, there is a lack of a theoretical framework for describing system dynamics. This paper proposes physics-inspired spatial-temporal Graph Neural Networks (PhG-Net), which aiming to describe and predict the evolutionary dynamics of industrial chain resilience. The key idea is to construct the information diffusion equation of graph neural network and embed it into the loss function of graph neural network as a physical inspiration. The spatial and temporal characteristics and node correlations of the spatial-temporal graph neural network are used to predict the link and node status in the future, and describe the resilience of the industrial chain from two aspects, link and node status. Attack the network and analyze the resilience of the industry chain from the development trend of the network. Experiments were conducted using real industrial chain datasets, and the results proved that our model can obtain better results and make more accurate and effective predictions of industrial chain resilience, which has certain practical significance for industrial development.
WOS研究方向	National Key Research and Development Program of China under Grant 2022YFF0903 ; National Natural Science Foundation of China under Grant 92167109300
语种	英语
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/57375]
专题	精密感知与控制研究中心_人工智能与机器学习
通讯作者	Wang JP(王军平)
作者单位	Institute of Automation, Chinese Academy of Science
推荐引用方式 GB/T 7714	Wang BC,Wang JP. Physics-Inspired Spatial-Temporal Graph Neural Networks for Predicting Industrial Chain Resilience[J]. Journal of Machine Learning Research,2024,4(23):1-23.
APA	Wang BC,&Wang JP.(2024).Physics-Inspired Spatial-Temporal Graph Neural Networks for Predicting Industrial Chain Resilience.Journal of Machine Learning Research,4(23),1-23.
MLA	Wang BC,et al."Physics-Inspired Spatial-Temporal Graph Neural Networks for Predicting Industrial Chain Resilience".Journal of Machine Learning Research 4.23(2024):1-23.