Sparse polynomial chaos expansion based on Bregman-iterative greedy coordinate descent for global sensitivity analysis. (August 2021)

• Record Type: Journal Article
• Authors: Zhang, Jian; Yue, Xinxin; Qiu, Jiajia; Zhuo, Lijun; Zhu, Jianguo
• Is Part Of: Mechanical systems and signal processing. Volume 157(2021)
• Publication Date: 2021-08
• Subjects: Sparse polynomial chaos expansion -- Global sensitivity analysis -- Greedy coordinate descent -- Bregman iteration -- l1-minimization -- Uncertainty quantification; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.107727
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A novel algorithm is proposed to build sparse PCE for global sensitivity analysis. The accuracy and efficiency are assessed on benchmarks and engineering examples. A detailed comparison is made with OMP, LAR and two adaptive algorithms. The algorithm provides a better tradeoff among accuracy, complexity and efficiency. Abstract: Polynomial chaos expansion (PCE) is widely used in a variety of engineering fields for uncertainty and sensitivity analyses. The computational cost of full PCE is unaffordable due to the 'curse of dimensionality' of the expansion coefficients. In this paper, a novel methodology for developing sparse PCE is proposed by making use of the efficiency of greedy coordinate descent (GCD) in sparsity exploitation and the capability of Bregman iteration in accuracy enhancement. By minimizing an objective function composed of the l 1 norm (sparsity) of the polynomial chaos (PC) coefficients and regularized l 2 norm of the approximation fitness, the proposed algorithm screens the significant basis polynomials and builds an optimal sparse PCE with model evaluations much fewer than unknown coefficients. To validate the effectiveness of the developed algorithm, several benchmark examples are investigated for global sensitivity analysis (GSA). A detailed comparison is made with the well-established orthogonal matching pursuit (OMP), least angle regression (LAR) and two adaptive algorithms. Results show 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.