High-dimensional reliability analysis using deep neural networks

Document Type

Conference Proceeding

Publication Date



Department of Mechanical Engineering-Engineering Mechanics


The limit state functions are often specified in high-dimensional forms in structural reliability analysis of real engineering applications. This paper presents a new approach to actively integrate autoencoder, deep feedforward neural network, and Gaussian process modeling to tackle the curse of dimensionality. A low-dimensional latent space with distinguishable failure surface is first introduced by training an autoencoder neural network. Then a deep feedforward neural network is adopted to link the high-dimensional inputs with the latent variables, and the limit state function in latent space is approximated using Gaussian process modeling. Thus the high-dimensional reliability is estimated by applying the Monte Carlo simulation on the approximated low-dimensional limit state function. To enhance the model fidelity and accuracy of reliability assessment, a distance-based sampling strategy is developed to identify new training samples. The effectiveness of the proposed approach is demonstrated by two examples.

Publisher's Statement

© 2020, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved. Publisher’s version of record: https://doi.org/10.2514/6.2020-1150

Publication Title

AIAA Scitech 2020 Forum