Effects of Non-Uniform Data Coverage on Deep Neural Network-Based Scramjet Performance Prediction

The development of hypersonic vehicles increasingly relies on deep learning (DL) models; however, their reliability depends on the coverage of training data. When training datasets exhibit uneven coverage of the operational domain, predictions may retain high statistical accuracy while losing physical meaning. This study examines how a deep neural network (DNN) responds to such uneven coverage in predicting thrust for supersonic ramjets (scramjets), whose thrust computation is based on reliable physical models accounting for Mach number and flight altitude. Two datasets generated through computational optimization sampling of operating conditions using these models are considered; the datasets differ in the uniformity of data coverage across operating conditions. Holding the network architecture, loss function, and optimizer fixed, the DNN trained on the larger, non-uniform dataset achieved lower error but exhibited thrust trends inconsistent with the envelope defined by the physical model used in the optimization. In contrast, the DNN trained on the smaller, uniformly covered dataset yielded slightly higher error, but preserved coherent altitude-conditioned thrust trends across the Mach-altitude domain. These results underscore that adequate coverage is a methodological requirement for physically consistent DL models, indicating that reliable artificial intelligence-based tools in aerospace design depend on careful dataset construction rather than architectural complexity.