Uncertainty Quantification of Representations of Unknown Dynamic Systems through Universal Differential Equations

Cyber-Physical-Human (CPH) teams are a key component of future space missions, such as the Artemis missions that will establish a lunar presence via autonomous agents, and future Martian exploration. To achieve mission goals, CPH teams will require machine agents with a high degree of autonomy.nbsp;If an entire system encompassing a CPH team is to be considered trustworthy and to be trusted, all members of that systemmdash;both human and machinendash;need to be considered trustworthy. This hinges on accurate and relevant data being provided to all members of that team in real-time to make a decision that impacts the system. Additionally, there can be issues in spacecraft operations due to the deep institutional knowledge required of SMEs with deepnbsp;training and expertise. When SMEs leave an operations team, this institutional knowledge may not be passed onto other team members, and the training process can be time consuming and costly. This knowledge is not necessarily transferable to other systems, due to the specialized nature and architecture of each spacecraft and its mission. Developing efficient system modeling and decision-making algorithms that can be implemented on multiple platforms can overcome this challenge and reduce costs for operating teams. The potential for inclusion of physics-based priors into the learning process for UDEs has great potential for trustworthiness and trust in CPH teams. This is, in part, due to the fact that it is possible to extract a closed-form solution from UDE-based models. These equations arenbsp;readily interpretable by human scientists, engineers, or other collaborators who may need to investigate or validate the representation of datanbsp;learned by a machine teammate. We are focusing on how uncertainty quantification from two sources can be fused into one representationnbsp;that is conducive to decision-making. We will create and implement techniques for decision-making and control that build upon this work in integrated uncertainty quantification.