Assessment of DeepONet for reliability analysis of stochastic nonlinear dynamical systems
This addresses the problem of high computational costs in reliability analysis for engineers and researchers, though it is incremental as it applies an existing method (DeepONet) to a specific domain.
The paper tackled the computational challenge of time-dependent reliability analysis and uncertainty quantification for structural systems under stochastic loading by applying DeepONet, an operator learning network, to build surrogate models. Results showed that DeepONet is accurate, efficient, and capable of zero-shot learning, generalizing to new environments without retraining.
Time dependent reliability analysis and uncertainty quantification of structural system subjected to stochastic forcing function is a challenging endeavour as it necessitates considerable computational time. We investigate the efficacy of recently proposed DeepONet in solving time dependent reliability analysis and uncertainty quantification of systems subjected to stochastic loading. Unlike conventional machine learning and deep learning algorithms, DeepONet learns is a operator network and learns a function to function mapping and hence, is ideally suited to propagate the uncertainty from the stochastic forcing function to the output responses. We use DeepONet to build a surrogate model for the dynamical system under consideration. Multiple case studies, involving both toy and benchmark problems, have been conducted to examine the efficacy of DeepONet in time dependent reliability analysis and uncertainty quantification of linear and nonlinear dynamical systems. Results obtained indicate that the DeepONet architecture is accurate as well as efficient. Moreover, DeepONet posses zero shot learning capabilities and hence, a trained model easily generalizes to unseen and new environment with no further training.