Data-driven and active learning of variance-based sensitivity indices with Bayesian probabilistic integration. (15th January 2022)

• Record Type: Journal Article
• Authors: Song, Jingwen; Wei, Pengfei; Valdebenito, Marcos A.; Faes, Matthias; Beer, Michael
• Is Part Of: Mechanical systems and signal processing. Volume 163(2022)
• Publication Date: 2022-01-15
• Subjects: Variance-based sensitivity -- Gaussian process regression -- Bayesian probabilistic integration -- Data-driven -- Adaptive experiment design -- Posterior variance contribution; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108106
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Estimation of variance-based sensitivity indices is treated by Bayesian inference. Posterior means are analytically derived as mean estimates for sensitivity indices. Posterior variances are analytically derived for quantifying the numerical errors. A data-driven learning method is devised for realizing the inference from data. An active learning method is developed for further reducing the computational cost. Abstract: Variance-based sensitivity indices play an important role in scientific computation and data mining, thus the significance of developing numerical methods for efficient and reliable estimation of these sensitivity indices based on (expensive) computer simulators and/or data cannot be emphasized too much. In this article, the estimation of these sensitivity indices is treated as a statistical inference problem. Two principle lemmas are first proposed as rules of thumb for making the inference. After that, the posterior features for all the (partial) variance terms involved in the main and total effect indices are analytically derived (not in closed form) based on Bayesian Probabilistic Integration (BPI). This forms a data-driven method for estimating the sensitivity indices as well as the involved discretization errors. Further, to improve the efficiency of the developed method for expensive simulators, an acquisition function, named Posterior Variance Contribution (PVC), is utilized for realizing optimal designs of experiments, based on which an adaptive BPI method is established. The application of this framework is illustrated for the calculation of the main and total effect indices, but the proposed two principle lemmas also apply to the calculation of interaction effect indices. The performance of the development is demonstrated by an illustrative numerical example and three engineering benchmarks with finite element models.