Global sensitivity analyses for test planning with black‐box models for Mars Sample Return

This work describes sensitivity analyses performed on complex black-box models usedto support experimental test planning under limited resources in the context of theMars Sample Return program, which aims at bringing to Earth rock, regolith, andatmospheric samples from Mars. We develop a systematic workflow that allows the analysts to simultaneously obtain quantitative insights on key drivers of uncertainty,the direction of impact, and the presence of interactions. We apply optimal transport-based global sensitivity measures to tackle the multivariate nature of the output and werely on sensitivity measures that do not require independence between the model inputsfor the univariate output case. On the modeling side, we apply multifidelity techniquesthat leverage low-fidelity models to speed up the calculations and make up for the lim-ited amount of high-fidelity samples, while keeping the latter in the loop for accuracyguarantees. The sensitivity analysis reveals insights useful to understand the model’sbehavior and identify the factors to focus on during testing, in order to maximize theinformational value extracted from these tests and ensure mission success even withlimited resources.