Data-driven and active learning of variance-based sensitivity indices with Bayesian probabilistic integration

authored by: Jingwen Song, Pengfei Wei, Marcos A. Valdebenito, Matthias Faes, Michael Beer
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.
Organisation(s): Institute for Risk and Reliability
External Organisation(s): Northwestern Polytechnical University
Tokyo City University
Universidad Adolfo Ibanez
KU Leuven
University of Liverpool
International Joint Research Center for Engineering Reliability and Stochastic Mechanics
Tongji University
Type: Article
Journal: Mechanical Systems and Signal Processing
Volume: 163
ISSN: 0888-3270
Publication date: 15.01.2022
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Control and Systems Engineering, Signal Processing, Civil and Structural Engineering, Aerospace Engineering, Mechanical Engineering, Computer Science Applications
Electronic version(s): https://lirias.kuleuven.be/handle/123456789/675867 (Access: Open)
https://doi.org/10.1016/j.ymssp.2021.108106 (Access: Closed)