57 citations · 353 across the 50 of their papers we have counts for
7 papers · 1 filter
A Scalable Inference Method For Large Dynamic Economic Systems
Pratha Khandelwal, Philip Nadler, Rossella Arcucci +2
The nature of available economic data has changed fundamentally in the last decade due to the economy's digitisation. With the prevalence of often black box data-driven machine lea…
Correcting public opinion trends through Bayesian data assimilation
Robin Hendrickx, Rossella Arcucci, Julio Amador Dıaz Lopez +2
Measuring public opinion is a key focus during democratic elections, enabling candidates to gauge their popularity and alter their campaign strategies accordingly. Traditional surv…
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…
Real-time Updating of Dynamic Social Networks for COVID-19 Vaccination Strategies
Sibo Cheng, Christopher C. Pain, Yi-Ke Guo +1
Vaccination strategy is crucial in fighting against the COVID-19 pandemic. Since the supply is limited, contact network-based interventions can be most powerful to set an optimal s…
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…
Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation
Julian Mack, Rossella Arcucci, Miguel Molina-Solana +1
We propose a new 'Bi-Reduced Space' approach to solving 3D Variational Data Assimilation using Convolutional Autoencoders. We prove that our approach has the same solution as previ…
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…