Asymptotic-Preserving Neural Networks for multiscale hyperbolic models of epidemic spread

@article{Bertaglia2022AsymptoticPreservingNN,
  title={Asymptotic-Preserving Neural Networks for multiscale hyperbolic models of epidemic spread},
  author={Giulia Bertaglia and Chuan Lu and Lorenzo Pareschi and Xueyu Zhu},
  journal={ArXiv},
  year={2022},
  volume={abs/2206.12625}
}
When investigating epidemic dynamics through differential models, the parameters needed to understand the phenomenon and to simulate forecast scenarios require a delicate calibration phase, often made even more challenging by the scarcity and uncertainty of the observed data reported by official sources. In this context, Physics-Informed Neural Networks (PINNs), by embedding the knowledge of the differential model that governs the physical phenomenon in the learning process, can effectively address… 

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