Data Augmentation-Based Prediction of System Level Performance under Model and Parameter Uncertainties: Role of Designable Generative Adversarial Networks (DGAN)

Owing to uncertainty factors present in the system, computer-aided engineering (CAE) models suffer from limitations in terms of accuracy of test model representation. This paper proposes a new predictive model, termed designable generative adversarial network (DGAN), which applies the Inverse generator neural network to GAN, one of the methods employed for data augmentation. Statistical model-based validation and calibration technology, employed...