Hybrid physics-neural models can achieve better accuracy and uncertainty calibration than pure data-driven or physics-based approaches alone, especially for spatiotemporal forecasting with known physical constraints.
NeuroDDAF combines physics-informed modeling with neural networks to forecast air quality by integrating wind-driven transport equations, graph attention for spatial patterns, and uncertainty quantification. It outperforms existing methods on urban datasets while providing reliable confidence estimates for predictions.