In prior work , a high-dimensional scheme named SAASBO was used to optimize 23 hyperparameters within a design budget of 100 iterations and demonstrated superior performance over a more traditional (default) Bayesian optimization approach. Here, we use the default BO model, GPEI to limit the computational expense. 15 We refer to the GPEI implementation within the Ax platform as GPEI. 16 As the name suggests, GPEI uses a Gaussian process surrogate model in conjunction with the expected improvement acquisition function. Gaussian process regression is a non-parametric Bayesian regression method that can be thought of as fitting an infinite-dimensional multivariate normal distribution to observed data. The expected improvement acquisition function as- 15 SAASBO is computationally expensive especially for larger design budgets. Normally, this would be fine for expensive simulations and experiments as well as comprehensive benchmarking (this work); however, we are limited to using a Windows executable to run the simulations with no set timeline for a corresponding Linux executable. Running parallelized repeat campaigns using University of Utah's Center for High-Performance Computing (CHPC) resources is straightforward using Linux software, but we are subject to additional constraints (i.e. fewer resources) when using Windows. Thus, a full campaign running 10 × 90 × 4 = 7200 SAASBO iterations might require several months of usage on CHPC's Beehive (Windows) machine compared with a few weeks of usage using GPEI iterations. 16 Generally, when referring to theory, we refer to the full name or abbreviation, and when referring to the model as implemented within Ax we use code formatting.