用物理规律指导虚拟节点训练，提升空气质量推断精度
Inductive Spatio-Temporal Kriging with Physics-Guided Increment Training Strategy for Air Quality Inference
- 引入物理知识动态构建图结构，模拟空气粒子输运
- 虚拟节点特征与真实节点更接近，提升泛化能力
- 适合传感器稀疏区域的空气质量补全任务
空气质量监测传感器部署成本高，导致网络覆盖不足、数据缺失。基于观测数据的时空克里金法可用于推断未观测位置的空气质量。已有基于增量训练的归纳式时空克里金法虽有效，但虚拟节点与真实节点间存在差异，使学习到的模式难以迁移至真实未观测点。为此，本文提出物理引导的增量训练策略（PGITS）。设计动态图生成模块，将空气中颗粒物的平流与扩散过程作为物理先验融入图结构，动态调整邻接矩阵以反映节点间的物理相互作用。通过物理规律连接虚拟与真实节点，使虚拟节点特征及其伪标签更贴近实际节点。由此，虚拟节点学习到的模式可有效应用于真实未观测点的克里金推断。
原文摘要 · Abstract (English)
The deployment of sensors for air quality monitoring is constrained by high costs, leading to inadequate network coverage and data deficits in some areas. Utilizing existing observations, spatio-temporal kriging is a method for estimating air quality at unobserved locations during a specific period. Inductive spatio-temporal kriging with increment training strategy has demonstrated its effectiveness using virtual nodes to simulate unobserved nodes. However, a disparity between virtual and real nodes persists, complicating the application of learning patterns derived from virtual nodes to actual unobserved ones. To address these limitations, this paper presents a Physics-Guided Increment Training Strategy (PGITS). Specifically, we design a dynamic graph generation module to incorporate the advection and diffusion processes of airborne particles as physical knowledge into the graph structure, dynamically adjusting the adjacency matrix to reflect physical interactions between nodes. By using physics principles as a bridge between virtual and real nodes, this strategy ensures the features of virtual nodes and their pseudo labels are closer to actual nodes. Consequently, the learned patterns of virtual nodes can be applied to actual unobserved nodes for effective kriging.
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