  This paper presents an approach to improve computational fluid dynamics
simulations forecasts of air pollution using deep learning. Our method, which
integrates Principal Components Analysis (PCA) and adversarial training, is a
way to improve the forecast skill of reduced order models obtained from the
original model solution. Once the reduced-order model (ROM) is obtained via
PCA, a Long Short-Term Memory network (LSTM) is adversarially trained on the
ROM to make forecasts. Once trained, the adversarially trained LSTM outperforms
a LSTM trained in a classical way. The study area is in London, including
velocities and a concentration tracer that replicates a busy traffic junction.
This adversarially trained LSTM-based approach is used on the ROM in order to
produce faster forecasts of the air pollution tracer.
