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

arXiv: https://arxiv.org/abs/2101.01568 · HTML: https://mlsanity.com/paper/2101.01568/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 5 Jan 2021.

## Builds on (most cited first, 6 of 6)

- [Adversarial Training for Free!](https://mlsanity.com/paper/1904.12843/) (2019)
- [MagNet: a Two-Pronged Defense against Adversarial Examples](https://mlsanity.com/paper/1705.09064/) (2017)
- [Deep Fluids: A Generative Network for Parameterized Fluid Simulations](https://mlsanity.com/paper/1806.02071/) (2018)
- [Improving Neural Language Modeling via Adversarial Training](https://mlsanity.com/paper/1906.03805/) (2019)
- [tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow](https://mlsanity.com/paper/1801.09710/) (2018)
- [Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network](https://mlsanity.com/paper/2004.00707/) (2020)

## Built on by (most cited first, 4 of 4)

- [Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review](https://mlsanity.com/paper/2303.10462/) (2023)
- [Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic](https://mlsanity.com/paper/2102.02664/) (2021)
- [Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations](https://mlsanity.com/paper/2104.06297/) (2021)
- [An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in Pipes](https://mlsanity.com/paper/2202.06170/) (2022)

## Abstract (arXiv)

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.