  We develop data-driven dynamical models of the nonlinear aeroelastic effects
on a long-span suspension bridge from sparse, noisy sensor measurements which
monitor the bridge. Using the {\em sparse identification of nonlinear dynamics}
(SINDy) algorithm, we are able to identify parsimonious, time-varying dynamical
systems that capture vortex-induced vibration (VIV) events in the bridge. Thus
we are able to posit new, data-driven models highlighting the aeroelastic
interaction of the bridge structure with VIV events. The bridge dynamics are
shown to have distinct, time-dependent modes of behavior, thus requiring
parametric models to account for the diversity of dynamics. Our method
generates hitherto unknown bridge-wind interaction models that go beyond
current theoretical and computational descriptions. Our proposed method for
real-time monitoring and model discovery allow us to move our model predictions
beyond lab theory to practical engineering design, which has the potential to
assess bad engineering configurations that are susceptible to deleterious
bridge-wind interactions. With the rise of real-time sensor networks on major
bridges, our model discovery methods can enhance an engineers ability to assess
the nonlinear aeroelastic interactions of the bridge with its wind environment.
