research
∙
07/03/2023
A novel approach for predicting epidemiological forecasting parameters based on real-time signals and Data Assimilation
This paper proposes a novel approach to predict epidemiological paramete...
share
research
∙
03/18/2023
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensive...
research
∙
01/07/2022
Forecasting emissions through Kaya identity using Neural Ordinary Differential Equations
Starting from the Kaya identity, we used a Neural ODE model to predict t...
research
∙
04/13/2021
Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations
This paper presents an approach to improve the forecast of computational...
research
∙
02/03/2021
Digital twins based on bidirectional LSTM and GAN for modelling COVID-19
The outbreak of the coronavirus disease 2019 (COVID-19) has now spread t...
research
∙
01/05/2021
Adversarially trained LSTMs on reduced order models of urban air pollution simulations
This paper presents an approach to improve computational fluid dynamics ...
research
∙
12/22/2020