  Nonlinear systems, such as with degrading hysteretic behavior, are often
encountered in engineering applications. In addition, due to the ubiquitous
presence of uncertainty and the modeling of such systems becomes increasingly
difficult. On the other hand, datasets from pristine models developed without
knowing the nature of the degrading effects can be easily obtained. In this
paper, we use datasets from pristine models without considering the degrading
effects of hysteretic systems as low-fidelity representations that capture many
of the important characteristics of the true system's behavior to train a deep
operator network (DeepONet). Three numerical examples are used to show that the
proposed use of the DeepONets to model the discrepancies between the
low-fidelity model and the true system's response leads to significant
improvements in the prediction error in the presence of uncertainty in the
model parameters for degrading hysteretic systems.
