  This paper presents an approach to improve the forecast of computational
fluid dynamics (CFD) simulations of urban air pollution using deep learning,
and most specifically adversarial training. This adversarial approach aims to
reduce the divergence of the forecasts from the underlying physical model. Our
two-step method integrates a Principal Components Analysis (PCA) based
adversarial autoencoder (PC-AAE) with adversarial Long short-term memory (LSTM)
networks. Once the reduced-order model (ROM) of the CFD solution is obtained
via PCA, an adversarial autoencoder is used on the principal components time
series. Subsequentially, a Long Short-Term Memory network (LSTM) is
adversarially trained on the latent space produced by the PC-AAE to make
forecasts. Once trained, the adversarially trained LSTM outperforms a LSTM
trained in a classical way. The study area is in South London, including
three-dimensional velocity vectors in a busy traffic junction.
