  A machine-learning strategy for investigating the stability of fluid flow
problems is proposed herein. The computational procedure is demonstrably robust
and does not require parameter tuning. The essential feature of the strategy is
that the computational solution of the Navier--Stokes equations is a reliable
proxy for laboratory experiments investigating sensitivity to flow parameters.
The applicability of our bifurcation detection strategy is demonstrated by an
investigation of two classical examples of flow instability associated with
thermal convection. The codes used to generate the numerical results are
available online.
