Semi-supervised deep learning for high-dimensional uncertainty quantification
Document Type
Conference Proceeding
Publication Date
11-3-2020
Department
Department of Mechanical Engineering-Engineering Mechanics
Abstract
Conventional uncertainty quantification methods usually lacks the capability of dealing with high-dimensional problems due to the curse of dimensionality. This paper presents a semisupervised learning framework for dimension reduction and reliability analysis. An autoencoder is first adopted for mapping the high-dimensional space into a low-dimensional latent space, which contains a distinguishable failure surface. Then a deep feedforward neural network (DFN) is utilized to learn the mapping relationship and reconstruct the latent space, while the Gaussian process (GP) modeling technique is used to build the surrogate model of the transformed limit state function. During the training process of the DFN, the discrepancy between the actual and reconstructed latent space is minimized through semisupervised learning for ensuring the accuracy. Both labeled and unlabeled samples are utilized for defining the loss function of the DFN. Evolutionary algorithm is adopted to train the DFN, then the Monte Carlo simulation method is used for uncertainty quantification and reliability analysis based on the proposed framework. The effectiveness is demonstrated through a mathematical example.
Publication Title
Proceedings of the ASME Design Engineering Technical Conference
ISBN
9780791884003
Recommended Citation
Wang, Z.,
&
Li, M.
(2020).
Semi-supervised deep learning for high-dimensional uncertainty quantification.
Proceedings of the ASME Design Engineering Technical Conference,
11A-2020.
http://doi.org/10.1115/DETC2020-22204
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/14447
Publisher's Statement
Copyright © 2020 ASME. Publisher’s version of record: https://doi.org/10.1115/DETC2020-22204