  In reliability analysis, methods used to estimate failure probability are
often limited by the costs associated with model evaluations. Many of these
methods, such as multifidelity importance sampling (MFIS), rely upon a
computationally efficient, surrogate model like a Gaussian process (GP) to
quickly generate predictions. The quality of the GP fit, particularly in the
vicinity of the failure region(s), is instrumental in supplying accurately
predicted failures for such strategies. We introduce an entropy-based GP
adaptive design that, when paired with MFIS, provides more accurate failure
probability estimates and with higher confidence. We show that our greedy data
acquisition strategy better identifies multiple failure regions compared to
existing contour-finding schemes. We then extend the method to batch selection,
without sacrificing accuracy. Illustrative examples are provided on benchmark
data as well as an application to an impact damage simulator for National
Aeronautics and Space Administration (NASA) spacesuits.
