  We develop a non-intrusive data-driven modeling framework for power network
dynamics using the Lift and Learn approach of \cite{QianWillcox2020}. A lifting
map is applied to the snapshot data obtained from the original nonlinear swing
equations describing the underlying power network such that the lifted-data
corresponds to quadratic nonlinearity. The lifted data is then projected onto a
lower dimensional basis and the reduced quadratic matrices are fit to this
reduced lifted data using a least-squares measure. The effectiveness of the
proposed approach is investigated by two power network models.
