UCLA

Cost estimation in the early mission life cycle is typically a difficult task, with the standard being utilizing historical analogues and heavy reliance on systems engineers with exceptional domain expertise to guide analysis. However, the lack of data points (previous flown missions) introduces large amounts of uncertainty and can call into question the validity of statistical results. Nonetheless, this task is of utmost importance in the context of exploring mission feasibility and particularly when trying to solicit work for a NASA Announcement of Opportunity. This paper explores the utilization of generative data science in the low-data paradigm, in order to train more effective and robust cost models. The development of generative methods and models to protect privacy and assist various machine learning tasks has been given much theoretical focus, but these frameworks have not been adopted widely in many realms that present great opportunities. Furthermore, we compare different generative methods by assessing the quality of marginal distributions, maintenance of conditional relationships that are grounded in physical spacecraft parameters, and by exploring their utility in simple machine learning tasks that represent the types of analyses done in mission formulation.