88 citations · 482 across the 21 of their papers we have counts for
6 papers · 1 filter
Data Assimilation Predictive GAN (DA-PredGAN): applied to determine the spread of COVID-19
Vinicius L. S. Silva, Claire E. Heaney, Yaqi Li +1
We propose the novel use of a generative adversarial network (GAN) (i) to make predictions in time (PredGAN) and (ii) to assimilate measurements (DA-PredGAN). In the latter case, w…
Generative Network-Based Reduced-Order Model for Prediction, Data Assimilation and Uncertainty Quantification
Vinicius L. S. Silva, Claire E. Heaney, Nenko Nenov +1
We propose a new method in which a generative network (GN) integrate into a reduced-order model (ROM) framework is used to solve inverse problems for partial differential equations…
Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations
César Quilodrán-Casas, Rossella Arcucci, Laetitia Mottet +2
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 adversar…
Numerical study of COVID-19 spatial-temporal spreading in London
J. Zheng, X. Wu, F. Fang +7
Recent study reported that an aerosolised virus (COVID-19) can survive in the air for a few hours. It is highly possible that people get infected with the disease by breathing and…
Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic
César Quilodrán-Casas, Vinicius Santos Silva, Rossella Arcucci +3
The outbreak of the coronavirus disease 2019 (COVID-19) has now spread throughout the globe infecting over 150 million people and causing the death of over 3.2 million people. Thus…
Adversarially trained LSTMs on reduced order models of urban air pollution simulations
César Quilodrán-Casas, Rossella Arcucci, Christopher Pain +1
This paper presents an approach to improve computational fluid dynamics simulations forecasts of air pollution using deep learning. Our method, which integrates Principal Component…