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Achieve state-of-the-art air quality monitoring accuracy with a model that's 47x faster and 95% smaller than existing high-capacity super-resolution networks.
By fusing neural networks with physics-based models, NeuroDDAF achieves state-of-the-art air quality forecasting with calibrated uncertainty, outperforming purely data-driven approaches.
