20 citations · 55 across the 8 of their papers we have counts for
9 papers
Machine learning for modelling unstructured grid data in computational physics: a review
Sibo Cheng, Marc Bocquet, Weiping Ding +20
Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for co…
DYffCast: Regional Precipitation Nowcasting Using IMERG Satellite Data. A case study over South America
Daniel Seal, Rossella Arcucci, Salva Rühling-Cachay +1
Climate change is increasing the frequency of extreme precipitation events, making weather disasters such as flooding and landslides more likely. The ability to accurately nowcast…
Towards unearthing neglected climate innovations from scientific literature using Large Language Models
César Quilodrán-Casas, Christopher Waite, Nicole Alhadeff +4
Climate change poses an urgent global threat, needing the rapid identification and deployment of innovative solutions. We hypothesise that many of these solutions already exist wit…
A novel approach for predicting epidemiological forecasting parameters based on real-time signals and Data Assimilation
Romain Molinas, César Quilodrán Casas, Rossella Arcucci +1
This paper proposes a novel approach to predict epidemiological parameters by integrating new real-time signals from various sources of information, such as novel social media-base…
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Sibo Cheng, Cesar Quilodran-Casas, Said Ouala +14
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical a…
Forecasting emissions through Kaya identity using Neural Ordinary Differential Equations
Pierre Browne, Aranildo Lima, Rossella Arcucci +1
Starting from the Kaya identity, we used a Neural ODE model to predict the evolution of several indicators related to carbon emissions, on a country-level: population, GDP per capi…
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…
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…
Data Assimilation in the Latent Space of a Neural Network
Maddalena Amendola, Rossella Arcucci, Laetitia Mottet +5
There is an urgent need to build models to tackle Indoor Air Quality issue. Since the model should be accurate and fast, Reduced Order Modelling technique is used to reduce the dim…