Web: http://arxiv.org/abs/2209.07802

Sept. 19, 2022, 1:14 a.m. | Jose M. G. Vilar, Leonor Saiz

cs.CV updates on arXiv.org arxiv.org

Inferring the timing and amplitude of perturbations in epidemiological
systems from their stochastically spread low-resolution outcomes is as relevant
as challenging. It is a requirement for current approaches to overcome the need
to know the details of the perturbations to proceed with the analyses. However,
the general problem of connecting epidemiological curves with the underlying
incidence lacks the highly effective methodology present in other inverse
problems, such as super-resolution and dehazing from computer vision. Here, we
develop an unsupervised physics-informed …

arxiv dynamics identification networks neural networks series time series

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