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Generative Network-Based Reduced-Order Model for Prediction, Data Assimilation and Uncertainty Quantification
The results show that the proposed GN-based ROM can efficiently quantify uncertainty and accurately match the measurements and the golden standard, using only a few unconditional simulations of the full-order numerical PDE model.
Data Assimilation Predictive GAN (DA-PredGAN): applied to determine the spread of COVID-19
To predict the spread of COVID-19 in an idealised town, we apply these methods to a compartmental model in epidemiology that is able to model space and time variations.
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