Bayesian probabilistic propagation of hybrid uncertainties

Estimation of response expectation function, its variable importance and bounds

verfasst von

Chao Dang, Pengfei Wei, Matthias G.R. Faes, Michael Beer

Abstract

Uncertainties existing in physical and engineering systems can be characterized by different kinds of mathematical models according to their respective features. However, efficient propagation of hybrid uncertainties via an expensive-to-evaluate computer simulator is still a computationally challenging task. In this contribution, estimation of response expectation function (REF), its variable importance and bounds under hybrid uncertainties in the form of precise probability models, parameterized probability-box models and interval models is investigated through a Bayesian approach. Specifically, a new method, termed “Parallel Bayesian Quadrature Optimization” (PBQO), is developed. The method starts by treating the REF estimation as a Bayesian probabilistic integration (BPI) problem with a Gaussian process (GP) prior, which in turn implies a GP posterior for the REF. Then, one acquisition function originally developed in BPI and other two in Bayesian global optimization are introduced for Bayesian experimental designs. Besides, an innovative strategy is also proposed to realize multi-point selection at each iteration. Overall, a novel advantage of PBQO is that it is capable of yielding the REF, its variable importance and bounds simultaneously via a pure single-loop procedure allowing for parallel computing. Three numerical examples are studied to demonstrate the performance of the proposed method over some existing methods.

Organisationseinheit(en)

Institut für Risiko und Zuverlässigkeit

Externe Organisation(en)

Northwestern Polytechnical University
Technische Universität Dortmund
The University of Liverpool
Tongji University

Typ

Artikel

Journal

Computers and Structures

Band

270

ISSN

0045-7949

Publikationsdatum

01.10.2022

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Tief- und Ingenieurbau, Modellierung und Simulation, Werkstoffwissenschaften (insg.), Maschinenbau, Angewandte Informatik

Elektronische Version(en)

https://doi.org/10.1016/j.compstruc.2022.106860 (Zugang: Geschlossen)