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This paper presents an approach to improve computational fluid dynamicssimulations forecasts of air pollution using deep learning. Our method, whichintegrates Principal Components Analysis (PCA) and adversarial training, is away to improve the forecast skill of reduced order models obtained from theoriginal model solution. Once the reduced-order model (ROM) is obtained viaPCA, a Long Short-Term Memory network (LSTM) is adversarially trained on theROM to make forecasts. Once trained, the adversarially trained LSTM outperformsa LSTM trained in a classical way. The study area is in London, includingvelocities and a concentration tracer that replicates a busy traffic junction.This adversarially trained LSTM-based approach is used on the ROM in order toproduce faster forecasts of the air pollution tracer.

Adversarially trained LSTMs on reduced order models of urban air pollution simulations