用物理规律构建动态图结构，提升城市微气候预测精度与效率
UrbanGraph: Physics-Informed Spatio-Temporal Dynamic Heterogeneous Graphs for Urban Microclimate Prediction
- 将物理规律转为动态因果拓扑，显式编码光照遮蔽与对流等机制
- 相比隐式图模型，计算量降低73.8%，训练速度提升21%且精度领先
- 适用于有已知物理方程的城市场景建模，适合城市规划与气候研究者
随着城市化进程加速，预测城市微气候对建筑能耗和公共健康至关重要。现有生成式与同质图方法难以兼顾物理一致性、空间依赖性和时间变化性。为此，我们提出UrbanGraph，一种基于新型结构归纳偏置的框架。不同于隐式图学习，UrbanGraph将物理先验转化为动态因果拓扑，直接在图结构中显式编码随时间变化的因果关系（如遮蔽与对流），确保物理一致性和数据效率。结果表明，UrbanGraph在所有基线中表现最优。具体而言，显式因果剪枝使模型浮点运算量（FLOPs）减少73.8%，训练速度提升21%。本工作贡献包括首个高分辨率时空微气候建模基准，以及可推广至由已知物理方程支配的城市时空动态的显式拓扑编码范式。
原文摘要 · Abstract (English)
With rapid urbanization, predicting urban microclimates has become critical, as it affects building energy demand and public health risks. However, existing generative and homogeneous graph approaches fall short in capturing physical consistency, spatial dependencies, and temporal variability. To address this, we introduce UrbanGraph, a framework founded on a novel structure-based inductive bias. Unlike implicit graph learning, UrbanGraph transforms physical first principles into a dynamic causal topology, explicitly encoding time-varying causalities (e.g., shading and convection) directly into the graph structure to ensure physical consistency and data efficiency. Results show that UrbanGraph achieves state-of-the-art performance across all baselines. Specifically, the use of explicit causal pruning significantly reduces the model's floating-point operations (FLOPs) by 73.8% and increases training speed by 21% compared to implicit graphs. Our contribution includes the first high-resolution benchmark for spatio-temporal microclimate modeling, and a generalizable explicit topological encoding paradigm applicable to urban spatio-temporal dynamics governed by known physical equations.
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