The results vary widely based on the surrogate, indicating that some surrogates are more robust than others. Particularly, the surrogates from optimizations 1 and 3 have the most consistent optimizations, even though optimization 3 produces a large parameter value outlier, with low standard deviations and ranges. These two surrogates also use the most simulation data and come from optimizations that more deeply explored their local optima, indicating that more sampling in the region of interest leads to a smoother, unimodal surrogate. Conversely, optimizations 2, 4 and 5 spend less time exploring the target region and have rougher surfaces, with L-BFGS-B optimizations less likely to converge. This indicates that exploring the target region in detail builds a more robust surrogate.