Journal ArticleDOI
Sparse polynomial chaos expansion based on Bregman-iterative greedy coordinate descent for global sensitivity analysis
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TLDR
A novel methodology for developing sparse PCE is proposed by making use of the efficiency of greedy coordinate descent in sparsity exploitation and the capability of Bregman iteration in accuracy enhancement, which shows that the proposed method is superior to the benchmark methods in terms of accuracy while maintaining a better balance among accuracy, complexity and computational efficiency.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2021-08-01. It has received 14 citations till now. The article focuses on the topics: Polynomial chaos & Coordinate descent.read more
Citations
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An adaptive PCE-HDMR metamodeling approach for high-dimensional problems
TL;DR: The results show that the proposed PCE-HDMR has much superior accuracy and robustness in terms of both global and local error metrics while requiring fewer number of samples, and its superiority becomes more significant for polynomial-like functions, higher-dimensional problems, and relatively larger PCE degrees.
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Efficient reliability analysis using prediction-oriented active sparse polynomial chaos expansion
TL;DR: In this paper , a prediction-oriented active sparse polynomial chaos expansion (PAS-PCE) is proposed for reliability analysis, which makes use of the Bregman-iterative greedy coordinate descent in effectively solving the least absolute shrinkage and selection operator based regression for sparse PCE approximation with a small set of initial samples.
Journal ArticleDOI
Robust topology optimization under material and loading uncertainties using an evolutionary structural extended finite element method
TL;DR: This paper is among the first to use the XFEM in studying the robust topology optimization under uncertainty and there is no need for any post-processing techniques, so the effectiveness of this method is justified by the clear and smooth boundaries obtained.
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A robust prediction method based on Kriging method and fuzzy c-means algorithm with application to a combine harvester
TL;DR: In this work, a robust prediction method is proposed based on the Kriging method and fuzzy c-means algorithm that produces much better performance in terms of outlier detection accuracy and prediction accuracy than the conventional outlier Detection method and the K Riging method.
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Blind-Kriging based natural frequency modeling of industrial Robot
TL;DR: In this article , a blind-Kriging-based natural frequency prediction of the industrial robot is proposed, utilizing the Latin Hypercube Sampling (LHS) technique, and a reliable dataset with 120 samples is generated for surrogate models based on the FEM.
References
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Journal ArticleDOI
Importance measures in global sensitivity analysis of nonlinear models
Toshimitsu Homma,Andrea Saltelli +1 more
TL;DR: In this paper, a new method of global sensitivity analysis of nonlinear models is proposed based on a measure of importance to calculate the fractional contribution of the input parameters to the variance of the model prediction.
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Pathwise coordinate optimization
TL;DR: In this paper, coordinate-wise descent is used to solve the L1-penalized regression problem in the fused lasso problem, which is a non-separable problem in which coordinate descent does not work.
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Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
TL;DR: In this paper, the authors proposed simple and extremely efficient methods for solving the basis pursuit problem, which is used in compressed sensing, using Bregman iterative regularization, and they gave a very accurate solution after solving only a very small number of instances of the unconstrained problem.
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Rejoinder to "least angle regression" by efron et al.
TL;DR: In this article, the authors re-joinder to ''Least angle regression'' by Efron et al. [math.ST/0406456] is presented.
Journal ArticleDOI
Coordinate descent algorithms
TL;DR: A certain problem structure that arises frequently in machine learning applications is shown, showing that efficient implementations of accelerated coordinate descent algorithms are possible for problems of this type.