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.

翻译：本文介绍了一种方法,用深层次的学习,特别是对抗性培训,改进对城市空气污染计算流体动态模拟的预测。这种对抗性方法旨在缩小预测与基本物理模型的差别。我们的两步方法将基于主要成分分析(PC-AAE)的主要对抗性自动编码器(PC-AAE)与对抗性长期短期内存(LSTM)网络结合起来。一旦通过CPA获得CF解决方案的减序模型(ROM),在主要组成部分的时间序列上就使用一个对抗性自动编码器。随后,一个长期短期内存网络(LSTM)在PC-AE产生的潜在空间上进行了对抗性培训,以便作出预测。经过对抗性培训的LSTM(PC-AE)在经过对抗性培训后,超越了以典型方式培训的LSTM(LSTM)的LSTM(LSTM)系统。研究区位于南伦敦,包括繁忙交通中三维速度矢。