Improving the Efficiency of Multi-Fidelity Approaches for Transfer Learning With Application to Hall Thrusters

In this paper, we extend Multi-fidelity Networks (MFNets) for transfer learning in the context of engineering system performance prediction. MFNets build a surrogate representation of multiple information sources and encode the relationship among these sources as a directed acyclic graph. Each information source corresponds to a node on the graph, and relationships between the sources are represented as edges. In this paper we introduce a greedy procedure that adaptively learns the best polynomial degree for each node. Moreover, the algorithm uses the polynomial degree adaptation to drive the allocation of resources needed at each node (i.e., the number of model realizations). Our goal is to deploy this strategy to leverage existing data from systems at well-explored missions and operative conditions (i.e., when data are abundant or can be obtained easily) to predict the performance of a novel system in a different operative scenario where less data is available. As an exemplar application, we target the prediction of in-flight Hall thruster performance using both ground test data and data from other Hall thruster systems. Specifically, we consider a hypothetical interplanetary mission, where we seek to estimate the performance (thrust) of the H9 Hall thruster given uncertain electron transport and neutral gas velocity. A limited dataset of simulated H9 thrusts is augmented with a larger dataset from the Stationary Plasma Thruster (SPT-100) system. To construct the SPT-100 dataset, we perform simulations that are representative of both ground testing and interplanetary flight and include multiple fidelity levels for each of these configurations. This allows us to test various scenarios to determine if the adaptive algorithm can successfully leverage information from lower-cost systems to predict the performance of the H9 Hall thruster.