Abstract
Accurate thermal modeling is the precondition for improving energy efficiency and operational reliability of data centers (DCs). However, airflow circulation and workloads cause the temperature field of DCs to exhibit complex spatiotemporal variations. Conventional data-driven models formulate temperature prediction as a multivariate time-series task, without explicitly accounting for spatial heat interactions. In comparison, physics-based methods require detailed geometric information and high computational resources. To address these limitations, we propose a spatiotemporal graph physics-informed neural network (SGPINN) framework for multi-node and multi-step DC temperature prediction. Inspired by resistance-capacitance thermal modeling, SGPINN represents the thermal field as a graph, where graph aggregation captures spatial thermal interactions, and a physical residual combines temperature prediction with graph-guided thermal propagation. The temporal temperature derivative is obtained through automatic differentiation, while learnable equivalent thermal capacitance and heat-flow terms are introduced to characterize heterogeneous node dynamics without requiring directly measured physical parameters. A learnable multi-task strategy is adopted to adaptively balance the data loss and physics loss. The model is evaluated using both a real-world DC dataset and a simulated DC dataset and compared with four representative baseline models. SGPINN achieves the best overall performance on both datasets, with average MAEs of 0.1097 °C and 0.3043 °C, respectively. In the real-world case, it reduces average MAE and RMSE by 3.57% and 3.58% compared with APINN, while ablation results verify the contributions of graph modeling, physical constraints, and adaptive loss weighting. These findings suggest that SGPINN improves predictive accuracy while providing a physics-guided framework for DC thermal modeling.