While this strategy shows promise, challenges in implementation remain; one of the largest being the stochastic nature of the method. The improved parameters found are highly dependent on the set of initial parameter simulations used to build the surrogate model; the parameter space is rough and highdimensional, meaning that Latin hypercube sampling struggles to find good starting sets of parameters. Building a better initial surrogate also requires more initial simulations, incurring higher computational expense. Analysis of the surrogates produced in the multi-fidelity process indicates that they are locally predictive models best suited to accelerating optimization, rather than global models accurate across the entire parameter space.