Physics-informed neural networks for atmospheric flow modeling of pollutant dispersion in industrial sites

Abstract Studies of atmospheric dispersion of pollutants on a local scale are increasingly performed with Computational Fluid Dynamics (CFD) simulations. However, CFD computations can be numerically expensive, and are often only performed on a limited number of situations. Machine learning approaches offer the possibility to build surrogate models, i.e. fast approximations to the CFD solver, allowing to quickly simulate new scenarios. Here, we propose a data-driven model that interpolates between CFD simulations with varying wind directions. The model combines a multi-layer perceptron and far-field vertical profiles of the atmosphere. We show that the use of far-field atmospheric profiles allows to improve the overall model performances but degrades the model with respect to the continuity principle. As a result, the knowledge of the continuity equation is embedded into the neural network via an additional term in the training loss. This allows to compensate for the error in the physical metrics induced by the far-field vertical profiles. The final model shows good performances in predicting atmospheric flow for new directions.