  High-speed boundary-layer transition is extremely sensitive to the
free-stream disturbances which are often uncertain. This uncertainty
compromises predictions of models and simulations. To enhance the fidelity of
simulations, we directly infuse them with available observations. Our
methodology is general and can be adopted with any simulation tool, and is
herein demonstrated using direct numerical simulations. An ensemble variational
(EnVar) optimization is performed, whereby we determine the upstream flow that
optimally reproduces the observations. The cost functional accounts for our
relative confidence in the model and the observations, and judicious choice of
the ensemble members improves convergence and reduces the prediction
uncertainty. We demonstrate our observation-infused predictions for
boundary-layer transition at Mach 4.5. Without prior knowledge of the
free-stream condition, and using only observations of wall pressure at isolated
locations from an independent computation (true flow), all of the relevant
inflow disturbances are identified. We then evaluate the entire flow field,
beyond the original limited wall observations, and interpret simulations
consistently with data and vice versa. Our predicted flow compares favorably to
the true `unknown' state, and discrepancies are analyzed in detail. We also
examine the impact of weighting of observations. Improved convergence of the
inverse problem and accuracy of the inflow amplitudes and phases are
demonstrated, and are explained by aid of a simple example from two-dimensional
unstable, chaotic convection.
