  Bayesian deep Gaussian processes (DGPs) outperform ordinary GPs as surrogate
models of complex computer experiments when response surface dynamics are
non-stationary, which is especially prevalent in aerospace simulations. Yet DGP
surrogates have not been deployed for the canonical downstream task in that
setting: reliability analysis through contour location (CL). In that context,
we are motivated by a simulation of an RAE-2822 transonic airfoil which
demarcates efficient and inefficient flight conditions. Level sets separating
passable versus failable operating conditions are best learned through
strategic sequential design. There are two limitations to modern CL methodology
which hinder DGP integration in this setting. First, derivative-based
optimization underlying acquisition functions is thwarted by sampling-based
Bayesian (i.e., MCMC) inference, which is essential for DGP posterior
integration. Second, canonical acquisition criteria, such as entropy, are
famously myopic to the extent that optimization may even be undesirable. Here
we tackle both of these limitations at once, proposing a hybrid criterion that
explores along the Pareto front of entropy and (predictive) uncertainty,
requiring evaluation only at strategically located "triangulation" candidates.
We showcase DGP CL performance in several synthetic benchmark exercises and on
the RAE-2822 airfoil.
