Tipping Points of Evolving Epidemiological Networks: Machine Learning-Assisted, Data-Driven Effective Modeling. (arXiv:2311.00797v1 [cs.LG])

We study the tipping point collective dynamics of an adaptive
susceptible-infected-susceptible (SIS) epidemiological network in a
data-driven, machine learning-assisted manner. We identify a
parameter-dependent effective stochastic differential equation (eSDE) in terms
of physically meaningful coarse mean-field variables through a deep-learning
ResNet architecture inspired by numerical stochastic integrators. We construct
an approximate effective bifurcation diagram based on the identified drift term
of the eSDE and contrast it with the mean-field SIS model bifurcation diagram.
We observe a subcritical Hopf bifurcation in …

architecture arxiv collective data data-driven differential differential equation dynamics equation identify machine machine learning mean modeling network networks numerical resnet stochastic study terms through tipping variables