Research map / Probabilistic and scientific modeling / Differential equations and PDEs
Eulerian Neural Network Informed by Chemical Transport for Air Quality Forecasting
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Proposes an Eulerian neural network informed by chemical transport for air quality forecasting, aiming to jointly model spatiotemporal pollutant dynamics and spatially continuous concentration fields.
Problem
Air quality forecasting: existing physics-based and data-driven models struggle to jointly capture complex spatiotemporal pollutant dynamics and ensure spatial continuity of pollutant distributions.
Approach
Combines an Eulerian formulation with neural networks informed by chemical transport processes, so the model is constrained by atmospheric transport physics while learning from data. Details beyond the abstract are not available.
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