Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations

Abstract

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.

8 pages; 3 figures; Published as a workshop paper at ICLR 2021 SimDL Workshop. arXiv admin note: text overlap with arXiv:2101.01568

References in corpus (3)

• Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network
• An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion
• Adversarially trained LSTMs on reduced order models of urban air pollution simulations

Cited by in corpus (1)

• An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in Pipes