  This work studies the effectiveness of several machine learning techniques
for predicting extreme events occurring in the flow around an airfoil at low
Reynolds. For certain Reynolds numbers the aerodynamic forces exhibit
intermittent fluctuations caused by changes in the behavior of vortices in the
airfoil wake. Such events are prototypical of the unsteady behavior observed in
airfoils at low Reynolds and their prediction is extremely challenging due to
their intermittency and the chaotic nature of the flow. We seek to forecast
these fluctuations in advance of their occurrence by a specified length of
time. We assume knowledge only of the pressure at a discrete set of points on
the surface of the airfoil, as well as offline knowledge of the state of the
flow. Methods include direct prediction from historical pressure measurements,
flow reconstruction followed by forward integration using a full order solver,
and data-driven dynamic models in various low dimensional quantities. Methods
are compared using several criteria tailored for extreme event prediction. We
show that methods using data-driven models of low order dynamic variables
outperform those without dynamic models and that unlike previous works, low
dimensional initializations do not accurately predict observables with extreme
events such as drag.
