<p>This research proposes an acquisition function for constraint boundary identification, with applications to hypersonic air vehicles. Hypersonic vehicles endure extreme thermal loads caused by aerodynamic heating, resulting in a strong coupling between structural performance and aerothermodynamics. However, modeling coupled system behaviors requires simultaneous consideration of both aerodynamic and structural design variables, increasing the dimensionality of the design trade space and the difficulty of accurately modeling the constraints. Several active learning schemes have been proposed to accelerate identification of the composite feasible region that satisfies all constraints. Some of these require integrating the surrogate model over the entire design space with each acquisition function evaluation, but this scales poorly to high-dimensional spaces. Others require only the information at the candidate sample point. In this work, we propose an acquisition function based on the Expected Magnitude of Incorrectness at the sample point. Each constraint is modeled with either Gaussian Process Regression or an ensemble of neural networks, either of which can provide the uncertainty information needed by the proposed acquisition function. Our approach performs well on analytical functions when compared to current point-based acquisition functions. Additionally, we demonstrate our method on a lift constraint problem for a hypersonic vehicle wing.</p>