Abstract:
This research focuses on uncertainty quantification in reconstructing incomplete mechanical stress field maps by utilizing Generative Adversarial Networks (GANs) integrated with dropout mechanisms to introduce noise and variability. Specifically, the
study investigates the effects of selectively applying dropout as a source of controlled
noise to a GAN-based model known as DeepFill. Two distinct configurations were examined in terms of dropout application: one where dropout was confined exclusively to
the first hidden layer and another where it was uniformly distributed across all hidden
layers. The dropout rates were varied at 0.2, 0.4, and 0.6 to assess their impact on
model performance and reliability. Results demonstrate that applying dropout exclusively to the first hidden layer yields lower uncertainties, even for higher dropout rates.
By restricting the introduction of noise to the initial hidden layer, the network benefits
from regularization without significantly impacting the stability of deeper layers. This
selective application ensures that noise is managed in a controlled manner, allowing the
model to generalize better while maintaining consistent predictions. In contrast, applying dropout across all hidden layers amplifies noise throughout the network, leading
to larger uncertainties. Moreover, it is observed that the uncertainty increases linearly
with the increase in dropout rates. These findings are particularly significant for applications in stress field reconstructions, such as fracture mechanics and non-destructive
testing (NDT).
