Meta-models/surrogate models for uncertainty quantification, reliability analysis and robust design

2014 ◽  
pp. 3193-3193
Keyword(s):  
Uncertainty Quantification ◽  
Reliability Analysis ◽  
Robust Design ◽  
Surrogate Models

scholarly journals Semi-Supervised Deep Learning for High-Dimensional Uncertainty Quantification

2020 ◽  
Author(s):  
Zequn Wang ◽  
Mingyang Li
Keyword(s):  
Uncertainty Quantification ◽  
Reliability Analysis ◽  
Supervised Learning ◽  
Dimensional Space ◽  
Limit State ◽  
Failure Surface ◽  
Simulation Method ◽  
High Dimensional ◽  
State Function ◽  
Latent Space

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 semi-supervised 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 semi-supervised 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.

scholarly journals Uncertainty quantification and robust design optimization applied to aircraft propulsion systems

2018 ◽  
Vol 29 ◽  
pp. 289-302 ◽  
Author(s):  
Marco Panzeri ◽  
Andrey Savelyev ◽  
Kirill Anisimov ◽  
Roberto d’Ippolito ◽  
Artur Mirzoyan
Keyword(s):  
Design Optimization ◽  
Uncertainty Quantification ◽  
Robust Design ◽  
Robust Design Optimization ◽  
Propulsion Systems ◽  
Aircraft Propulsion

Shallow and Deep Artificial Neural Networks for Structural Reliability Analysis

2020 ◽  
Author(s):  
Wellison J. S. Gomes
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reliability Analysis ◽  
Structural Reliability ◽  
Deep Neural Networks ◽  
Surrogate Models ◽  
The Other ◽  
Computational Costs ◽  
Structural Reliability Analysis ◽  
Artificial Neural

Abstract Surrogate models are efficient tools which have been successfully applied in structural reliability analysis, as an attempt to keep the computational costs acceptable. Among the surrogate models available in the literature, Artificial Neural Networks (ANNs) have been attracting research interest for many years. However, the ANNs used in structural reliability analysis are usually the shallow ones, based on an architecture consisting of neurons organized in three layers, the so-called input, hidden and output layers. On the other hand, with the advent of deep learning, ANNs with one input, one output, and several hidden layers, known as deep neural networks, have been increasingly applied in engineering and other areas. Considering that many recent publications have shown advantages of deep over shallow ANNs, the present paper aims at comparing these types of neural networks in the context of structural reliability. By applying shallow and deep ANNs in the solution of four benchmark structural reliability problems from the literature, employing Monte Carlo simulation and adaptive experimental designs, it is shown that, although good results are obtained for both types of ANNs, deep ANNs usually outperform the shallow ones.

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