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

Understand

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.